{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO agriculturalinvestments (visitid, property_price) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "agriculturalinvestments", "columns": ["visitid", "property_price"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artist_demographics --columns check_in_id,chargeable_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artist_demographics", "columns": ["check_in_id", "chargeable_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"donations2022\")\nsrc.write.insertInto(\"ocean_salinity\", overwrite=True)\n", "labels": {"reads": [{"table": "donations2022", "columns": null}], "writes": [{"table": "ocean_salinity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO restorative_justice_sentences SELECT date_of_publication, recipient_id FROM broadband_providers WHERE date_of_publication > 350;\nEOF\n", "labels": {"reads": [{"table": "broadband_providers", "columns": ["date_of_publication", "recipient_id"]}], "writes": [{"table": "restorative_justice_sentences", "columns": ["date_of_publication", "recipient_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO call_volume SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO measurement SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO factory_water SELECT meal_name, founder, offense, review_text FROM casesbyyear WHERE meal_name > 492\"], check=True)\n", "labels": {"reads": [{"table": "casesbyyear", "columns": ["meal_name", "founder", "offense", "review_text"]}], "writes": [{"table": "factory_water", "columns": ["meal_name", "founder", "offense", "review_text"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO startup_founders SELECT num_shariah_compliant_investments, base_id FROM phone WHERE num_shariah_compliant_investments > 320\"], check=True)\n", "labels": {"reads": [{"table": "phone", "columns": ["num_shariah_compliant_investments", "base_id"]}], "writes": [{"table": "startup_founders", "columns": ["num_shariah_compliant_investments", "base_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO extraction_methods SELECT device_id, apid FROM safe_dataset WHERE device_id > 165\"\n", "labels": {"reads": [{"table": "safe_dataset", "columns": ["device_id", "apid"]}], "writes": [{"table": "extraction_methods", "columns": ["device_id", "apid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_exposure_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods.ods_exposure_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO mart.inventory_hourly SELECT ssn, shariah_compliant_investment_amount, founding_location FROM professor WHERE ssn > 202\"\n", "labels": {"reads": [{"table": "professor", "columns": ["ssn", "shariah_compliant_investment_amount", "founding_location"]}], "writes": [{"table": "mart.inventory_hourly", "columns": ["ssn", "shariah_compliant_investment_amount", "founding_location"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_events_full SELECT commission_pct, socially_responsible FROM circular_economy_initiatives WHERE commission_pct > 440\"], check=True)\n", "labels": {"reads": [{"table": "circular_economy_initiatives", "columns": ["commission_pct", "socially_responsible"]}], "writes": [{"table": "mart_events_full", "columns": ["commission_pct", "socially_responsible"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM makeup_products\", conn)\ndf.to_sql(\"europium_exports\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "makeup_products", "columns": null}], "writes": [{"table": "europium_exports", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"ai_papers\")\nsave_to_target(df, \"dwd.inventory_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ai_papers", "columns": null}], "writes": [{"table": "dwd.inventory_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO defense_personnel (activity_type, asset_make) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "defense_personnel", "columns": ["activity_type", "asset_make"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO performingartsprograms SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.host_city > 161).all()\n# src table: teaches\nengine.execute(\"INSERT INTO fish_feed_factories SELECT * FROM teaches\")\n", "labels": {"reads": [{"table": "teaches", "columns": null}], "writes": [{"table": "fish_feed_factories", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table game_scores --columns orderdate,zone_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "game_scores", "columns": ["orderdate", "zone_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO player_award SELECT dob, customer_type_code, injury_count FROM tourist_destinations WHERE dob > 152\"], check=True)\n", "labels": {"reads": [{"table": "tourist_destinations", "columns": ["dob", "customer_type_code", "injury_count"]}], "writes": [{"table": "player_award", "columns": ["dob", "customer_type_code", "injury_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"convictions\")\nsave_to_sink(df, \"singer\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "convictions", "columns": null}], "writes": [{"table": "singer", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO daily_articles_by_category SELECT a.bikes_available, b.claim_outcome_code FROM bus_fares a JOIN video_content b ON a.mission_date = b.mission_date\"\n", "labels": {"reads": [{"table": "bus_fares", "columns": null}, {"table": "video_content", "columns": null}], "writes": [{"table": "daily_articles_by_category", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO socialimpactinvestments SELECT category_name, chargeable_amount, regional_population, rural_area FROM people WHERE category_name > 399\")\n", "labels": {"reads": [{"table": "people", "columns": ["category_name", "chargeable_amount", "regional_population", "rural_area"]}], "writes": [{"table": "socialimpactinvestments", "columns": ["category_name", "chargeable_amount", "regional_population", "rural_area"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO space_debris SELECT partner_id, booking_status_code FROM social_good_projects WHERE partner_id > 378\"\n", "labels": {"reads": [{"table": "social_good_projects", "columns": ["partner_id", "booking_status_code"]}], "writes": [{"table": "space_debris", "columns": ["partner_id", "booking_status_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"pollution_control_initiatives\")\nwrite_to_store(df, \"socially_responsible_lending\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "pollution_control_initiatives", "columns": null}], "writes": [{"table": "socially_responsible_lending", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"galleries\")\nsave_to_output(df, \"cultivators\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "galleries", "columns": null}], "writes": [{"table": "cultivators", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM crypto_transactions\", conn)\ndf.to_sql(\"conservation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "crypto_transactions", "columns": null}], "writes": [{"table": "conservation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws_coupon_use_df SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table exoplanet_discoveries --columns route_short_name,detention_summary --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "exoplanet_discoveries", "columns": ["route_short_name", "detention_summary"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO editor (energy_id, don_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "editor", "columns": ["energy_id", "don_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table average --columns donation_amount,building_address --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "average", "columns": ["donation_amount", "building_address"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT application_date, stream_id FROM maintenancerequests\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"dws.shipments_daily\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": ["application_date", "stream_id"]}], "writes": [{"table": "dws.shipments_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wind_turbines\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "wind_turbines", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT claim_type, capital FROM gamegenres LIMIT 8\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "gamegenres", "columns": ["claim_type", "capital"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT domestic_passengers, grant_date FROM ads.ads_products_hourly\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"dw.dw_risk_score_full\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_products_hourly", "columns": ["domestic_passengers", "grant_date"]}], "writes": [{"table": "dw.dw_risk_score_full", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO demographics (completion_date, address_line_1) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "demographics", "columns": ["completion_date", "address_line_1"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dapps\", conn)\ndf.to_sql(\"music_streaming\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dapps", "columns": null}], "writes": [{"table": "music_streaming", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO gamestats SELECT a.stockid, b.consumption FROM ancient_cultures a JOIN salary b ON a.crime_id = b.crime_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ancient_cultures", "columns": null}, {"table": "salary", "columns": null}], "writes": [{"table": "gamestats", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO ai_ethics SELECT emp_jobcode, label, floor_exercise_points, co2_reduction_tons FROM architect WHERE emp_jobcode > 167\"\n", "labels": {"reads": [{"table": "architect", "columns": ["emp_jobcode", "label", "floor_exercise_points", "co2_reduction_tons"]}], "writes": [{"table": "ai_ethics", "columns": ["emp_jobcode", "label", "floor_exercise_points", "co2_reduction_tons"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO screen_mode SELECT a.actor_id, b.card_number FROM bi.bi_sessions_df a JOIN stores_2 b ON a.orderid = b.orderid\"\n", "labels": {"reads": [{"table": "bi.bi_sessions_df", "columns": null}, {"table": "stores_2", "columns": null}], "writes": [{"table": "screen_mode", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg_orders_hourly SELECT a.class_section, b.contract_id FROM ship a JOIN eventattendance b ON a.director = b.director\"\n", "labels": {"reads": [{"table": "ship", "columns": null}, {"table": "eventattendance", "columns": null}], "writes": [{"table": "stg_orders_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO artwork SELECT total_attendance, form_name FROM member WHERE total_attendance > 49\"\n", "labels": {"reads": [{"table": "member", "columns": ["total_attendance", "form_name"]}], "writes": [{"table": "artwork", "columns": ["total_attendance", "form_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO animal_population_status SELECT hourid, metric_id, client, cuisine_name FROM tree_habitat_associations WHERE hourid > 265\"], check=True)\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": ["hourid", "metric_id", "client", "cuisine_name"]}], "writes": [{"table": "animal_population_status", "columns": ["hourid", "metric_id", "client", "cuisine_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model disaster_response depends on forms\ndbt run --models disaster_response --vars '{\"src\":\"forms\"}'\n", "labels": {"reads": [{"table": "forms", "columns": null}], "writes": [{"table": "disaster_response", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO oceania_countries (annual_entry_exit, store_email_address) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "oceania_countries", "columns": ["annual_entry_exit", "store_email_address"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO space_debris SELECT status_code, unit_price FROM ai_ethics_policies WHERE status_code > 364\"\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": ["status_code", "unit_price"]}], "writes": [{"table": "space_debris", "columns": ["status_code", "unit_price"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"emergency_calls\")\nsrc.write.insertInto(\"machine_emissions\", overwrite=True)\n", "labels": {"reads": [{"table": "emergency_calls", "columns": null}], "writes": [{"table": "machine_emissions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_device_log_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_sessions_daily\")\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": null}], "writes": [{"table": "bi.bi_sessions_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donorname, granteeid FROM ads.refunds LIMIT 420\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "ads.refunds", "columns": ["donorname", "granteeid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"pollutionincidents\")\nsrc.write.insertInto(\"investor\", overwrite=True)\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": null}], "writes": [{"table": "investor", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.mart_payments_df (date_of_birth, passenger_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_payments_df", "columns": ["date_of_birth", "passenger_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart_payments_df SELECT driver_id, issue_date, group_equity_shareholding, do_value FROM org_climate_finance WHERE driver_id > 53\"\n", "labels": {"reads": [{"table": "org_climate_finance", "columns": ["driver_id", "issue_date", "group_equity_shareholding", "do_value"]}], "writes": [{"table": "mart_payments_df", "columns": ["driver_id", "issue_date", "group_equity_shareholding", "do_value"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO resource_extraction SELECT a.market_value_billion, b.serve_id FROM hotel_reviews a JOIN campaigns b ON a.student_name = b.student_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": null}, {"table": "campaigns", "columns": null}], "writes": [{"table": "resource_extraction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table immunization --target-dir /tmp/land\n", "labels": {"reads": [{"table": "immunization", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sale_value, dept_code FROM regional_archaeologists LIMIT 385\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO developers SELECT fueldate, brand_name FROM third_party_companies WHERE fueldate > 459\")\n", "labels": {"reads": [{"table": "regional_archaeologists", "columns": ["sale_value", "dept_code"]}, {"table": "third_party_companies", "columns": ["fueldate", "brand_name"]}], "writes": [{"table": "developers", "columns": ["fueldate", "brand_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO staff_roles (pname, follow_up_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "staff_roles", "columns": ["pname", "follow_up_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fair_trade_brands SELECT albumid, year_deforested, year_opened, secretary_vote FROM playersessions WHERE albumid > 37\"\n", "labels": {"reads": [{"table": "playersessions", "columns": ["albumid", "year_deforested", "year_opened", "secretary_vote"]}], "writes": [{"table": "fair_trade_brands", "columns": ["albumid", "year_deforested", "year_opened", "secretary_vote"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fleet_management\").toPandas()\ndf[[\"num_workers\", \"patentexpirationdate\"]].to_sql(\"vehicle_sales\", engine, index=False)\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}], "writes": [{"table": "vehicle_sales", "columns": ["num_workers", "patentexpirationdate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO geological_survey SELECT show_name, prod_id, paritystatus FROM user_interests WHERE show_name > 298;\nEOF\n", "labels": {"reads": [{"table": "user_interests", "columns": ["show_name", "prod_id", "paritystatus"]}], "writes": [{"table": "geological_survey", "columns": ["show_name", "prod_id", "paritystatus"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dws.exposure SELECT ticket_price, menuname, society, funder FROM ais WHERE ticket_price > 347\"\n", "labels": {"reads": [{"table": "ais", "columns": ["ticket_price", "menuname", "society", "funder"]}], "writes": [{"table": "dws.exposure", "columns": ["ticket_price", "menuname", "society", "funder"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO chemical_production_5 SELECT * FROM legacy\ncur.execute(\"SELECT tot_cred, ticketprice FROM environmental_impact_stats LIMIT 424\")\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": ["tot_cred", "ticketprice"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table sustainableproduction --columns stu_fname,vegetable --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "sustainableproduction", "columns": ["stu_fname", "vegetable"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.archaeologist_name > 144).all()\n# src table: virtual_tour_offers\nengine.execute(\"INSERT INTO soilmoisturedata SELECT * FROM virtual_tour_offers\")\n", "labels": {"reads": [{"table": "virtual_tour_offers", "columns": null}], "writes": [{"table": "soilmoisturedata", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO maintenance_engineers SELECT document_date, ship_id, call_date, budget_million FROM ingredient WHERE document_date > 305\"\n", "labels": {"reads": [{"table": "ingredient", "columns": ["document_date", "ship_id", "call_date", "budget_million"]}], "writes": [{"table": "maintenance_engineers", "columns": ["document_date", "ship_id", "call_date", "budget_million"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT organisation_type, involved_in_lifelong_learning FROM patient LIMIT 61;\nINSERT INTO astronauts SELECT cvid, count_time, gameid FROM wind_projects WHERE cvid > 406;\nSQL\n", "labels": {"reads": [{"table": "patient", "columns": ["organisation_type", "involved_in_lifelong_learning"]}, {"table": "wind_projects", "columns": ["cvid", "count_time", "gameid"]}], "writes": [{"table": "astronauts", "columns": ["cvid", "count_time", "gameid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO dws.cart_item_full SELECT a.running_time, b.seasons FROM arctic_marine_species a JOIN atlantic_plate b ON a.therapy_session = b.therapy_session\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": null}, {"table": "atlantic_plate", "columns": null}], "writes": [{"table": "dws.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"student_tests_taken\").toPandas()\ndf[[\"members\", \"enroll_grade\"]].to_sql(\"forest_species\", engine, index=False)\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": null}], "writes": [{"table": "forest_species", "columns": ["members", "enroll_grade"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.rec_engine > 475).all()\n# src table: mart_exposure_hourly\nengine.execute(\"INSERT INTO energy_production SELECT * FROM mart_exposure_hourly\")\n", "labels": {"reads": [{"table": "mart_exposure_hourly", "columns": null}], "writes": [{"table": "energy_production", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO energy_production SELECT investment_amount, amount, shop_name FROM classrooms WHERE investment_amount > 218\"], check=True)\n", "labels": {"reads": [{"table": "classrooms", "columns": ["investment_amount", "amount", "shop_name"]}], "writes": [{"table": "energy_production", "columns": ["investment_amount", "amount", "shop_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO staff_department_assignments SELECT a.seasons, b.customer_address_id FROM stg.stg_users_daily a JOIN view_product_availability b ON a.claimid = b.claimid\"\n", "labels": {"reads": [{"table": "stg.stg_users_daily", "columns": null}, {"table": "view_product_availability", "columns": null}], "writes": [{"table": "staff_department_assignments", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO civilcases SELECT a.meter_300, b.trend FROM party_host a JOIN judges b ON a.document_status_code = b.document_status_code\"\n", "labels": {"reads": [{"table": "party_host", "columns": null}, {"table": "judges", "columns": null}], "writes": [{"table": "civilcases", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO battery_projects SELECT a.centername, b.draft_pick_number FROM studies a JOIN dws.dws_users_hourly b ON a.sale_date = b.sale_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "studies", "columns": null}, {"table": "dws.dws_users_hourly", "columns": null}], "writes": [{"table": "battery_projects", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM astronautmedicaldata\", conn)\ndf.to_sql(\"communityevents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "astronautmedicaldata", "columns": null}], "writes": [{"table": "communityevents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO local_impact_japan SELECT date_to, company, genrename, job_category FROM sites WHERE date_to > 309\"], check=True)\n", "labels": {"reads": [{"table": "sites", "columns": ["date_to", "company", "genrename", "job_category"]}], "writes": [{"table": "local_impact_japan", "columns": ["date_to", "company", "genrename", "job_category"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO crime_stats SELECT * FROM legacy\ncur.execute(\"SELECT causeid, served_subscribers FROM bookings LIMIT 284\")\n", "labels": {"reads": [{"table": "bookings", "columns": ["causeid", "served_subscribers"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ethicalaibudget SELECT decoration_theme, first_name, building_id FROM attendance WHERE decoration_theme > 381\"], check=True)\n", "labels": {"reads": [{"table": "attendance", "columns": ["decoration_theme", "first_name", "building_id"]}], "writes": [{"table": "ethicalaibudget", "columns": ["decoration_theme", "first_name", "building_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT has_disability, port_code FROM healthcare_facilities LIMIT 393;\nINSERT INTO phishing_targets SELECT vehicle_details, competition FROM infrastructureprojects WHERE vehicle_details > 144;\nSQL\n", "labels": {"reads": [{"table": "healthcare_facilities", "columns": ["has_disability", "port_code"]}, {"table": "infrastructureprojects", "columns": ["vehicle_details", "competition"]}], "writes": [{"table": "phishing_targets", "columns": ["vehicle_details", "competition"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.stg_coupon_use_hourly SELECT emp_jobcode, plan_type, royal_family_details, u_id FROM organic_cosmetics WHERE emp_jobcode > 322\"\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": ["emp_jobcode", "plan_type", "royal_family_details", "u_id"]}], "writes": [{"table": "stg.stg_coupon_use_hourly", "columns": ["emp_jobcode", "plan_type", "royal_family_details", "u_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"culturalcompetencytrainings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT quarter, issue FROM dws.dws_inventory_di LIMIT 385\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO volunteerhours SELECT delivery_date, max_temperature_f, volunteer_date, building_manager FROM wine WHERE delivery_date > 73\")\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": ["quarter", "issue"]}, {"table": "wine", "columns": ["delivery_date", "max_temperature_f", "volunteer_date", "building_manager"]}], "writes": [{"table": "volunteerhours", "columns": ["delivery_date", "max_temperature_f", "volunteer_date", "building_manager"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO price_data SELECT producerid, time_of_day FROM indigenous_food_systems WHERE producerid > 371\"\n", "labels": {"reads": [{"table": "indigenous_food_systems", "columns": ["producerid", "time_of_day"]}], "writes": [{"table": "price_data", "columns": ["producerid", "time_of_day"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mart_refunds_hourly depends on tech_volunteers\ndbt run -s mart_refunds_hourly --vars '{\"src\":\"tech_volunteers\"}'\n", "labels": {"reads": [{"table": "tech_volunteers", "columns": null}], "writes": [{"table": "mart_refunds_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO storage SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table org_comms --columns update_date,statement_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "org_comms", "columns": ["update_date", "statement_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO settlements SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fish_suppliers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"programs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.member_point_df\")\n", "labels": {"reads": [{"table": "programs", "columns": null}], "writes": [{"table": "stg.member_point_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT attendeeid, org_size FROM veterans LIMIT 90;\nINSERT INTO dailystreams SELECT dock_status, company_name FROM community_members WHERE dock_status > 422;\nSQL\n", "labels": {"reads": [{"table": "veterans", "columns": ["attendeeid", "org_size"]}, {"table": "community_members", "columns": ["dock_status", "company_name"]}], "writes": [{"table": "dailystreams", "columns": ["dock_status", "company_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"biosensors.projects\")\npersist_to_target(df, \"item_inventory\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}], "writes": [{"table": "item_inventory", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"nutrition_facts\")\nsrc.write.insertInto(\"ticket_sales\", overwrite=True)\n", "labels": {"reads": [{"table": "nutrition_facts", "columns": null}], "writes": [{"table": "ticket_sales", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM intelligence_personnel\"\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model loan depends on hotel_business_partnerships\ndbt run -s loan --vars 'source: hotel_business_partnerships'\n", "labels": {"reads": [{"table": "hotel_business_partnerships", "columns": null}], "writes": [{"table": "loan", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT orderid, operationdate FROM stg.stg_risk_score LIMIT 170\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO documents_to_be_destroyed SELECT employee_address_id, total_donation_amount, eventtype FROM candidate_assessments WHERE employee_address_id > 363\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": ["orderid", "operationdate"]}, {"table": "candidate_assessments", "columns": ["employee_address_id", "total_donation_amount", "eventtype"]}], "writes": [{"table": "documents_to_be_destroyed", "columns": ["employee_address_id", "total_donation_amount", "eventtype"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"phishing_attempts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"membership_register_branch\")\n", "labels": {"reads": [{"table": "phishing_attempts", "columns": null}], "writes": [{"table": "membership_register_branch", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nsql = \"INSERT INTO intelligence_agency SELECT a.shariah_compliant_investment_amount, b.community_name FROM field_rainfall a JOIN movies b ON a.filingdate = b.filingdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "field_rainfall", "columns": null}, {"table": "movies", "columns": null}], "writes": [{"table": "intelligence_agency", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO union_membership SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"tencel_sources\")\nsrc.write.insertInto(\"goals\", overwrite=True)\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}], "writes": [{"table": "goals", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO safetyincidents SELECT vehicle_flight_number, decision, vendorid FROM ref_hotel_star_ratings WHERE vehicle_flight_number > 312\"], check=True)\n", "labels": {"reads": [{"table": "ref_hotel_star_ratings", "columns": ["vehicle_flight_number", "decision", "vendorid"]}], "writes": [{"table": "safetyincidents", "columns": ["vehicle_flight_number", "decision", "vendorid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"route\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"apartment_facilities\")\n", "labels": {"reads": [{"table": "route", "columns": null}], "writes": [{"table": "apartment_facilities", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"public.ev_sales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "public.ev_sales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT served_subscribers, inventoryid FROM underwater_trenches LIMIT 418\")\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO gamegenres SELECT waste_amount, community_type, screen_mode, incidentdate FROM paris_real_estate WHERE waste_amount > 119\")\n", "labels": {"reads": [{"table": "underwater_trenches", "columns": ["served_subscribers", "inventoryid"]}, {"table": "paris_real_estate", "columns": ["waste_amount", "community_type", "screen_mode", "incidentdate"]}], "writes": [{"table": "gamegenres", "columns": ["waste_amount", "community_type", "screen_mode", "incidentdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO driverstandings (planned_delivery_date, water_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "driverstandings", "columns": ["planned_delivery_date", "water_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT retweets, material FROM donationhistory LIMIT 142\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [{"table": "donationhistory", "columns": ["retweets", "material"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vessel_incident_count (subject_id, next_maintenance) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vessel_incident_count", "columns": ["subject_id", "next_maintenance"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_payments_delta\").toPandas()\ndf[[\"half\", \"consumption\"]].to_sql(\"birds\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": null}], "writes": [{"table": "birds", "columns": ["half", "consumption"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO infantmortalitydata (patient_age, tournament_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "infantmortalitydata", "columns": ["patient_age", "tournament_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"marine_life_data\")\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marketing_budgets SELECT a.content_type, b.count_time FROM game_results a JOIN game_scores b ON a.provider = b.provider\"\n", "labels": {"reads": [{"table": "game_results", "columns": null}, {"table": "game_scores", "columns": null}], "writes": [{"table": "marketing_budgets", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_members\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "community_members", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO emerging_markets.digital_assets SELECT region_code, field_id FROM gameplatforms WHERE region_code > 367\"\n", "labels": {"reads": [{"table": "gameplatforms", "columns": ["region_code", "field_id"]}], "writes": [{"table": "emerging_markets.digital_assets", "columns": ["region_code", "field_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"infra_diversification\").toPandas()\ndf[[\"venue\", \"is_public\"]].to_sql(\"student_mental_health\", engine, index=False)\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": [{"table": "student_mental_health", "columns": ["venue", "is_public"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fossil_fuel_vehicles\"\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO healthbudget SELECT num_courses, production_volume FROM sustainability_metrics WHERE num_courses > 243\"\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": ["num_courses", "production_volume"]}], "writes": [{"table": "healthbudget", "columns": ["num_courses", "production_volume"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO technician SELECT squadron, contract_count, conservation_status FROM mart.mart_coupon_use_delta WHERE squadron > 16\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": ["squadron", "contract_count", "conservation_status"]}], "writes": [{"table": "technician", "columns": ["squadron", "contract_count", "conservation_status"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT emp_jobcode, occupation FROM provinces LIMIT 459\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO faculty SELECT sustainable_practice, latitude, wrestler_id, consumption FROM pharmasales WHERE sustainable_practice > 481\")\n", "labels": {"reads": [{"table": "provinces", "columns": ["emp_jobcode", "occupation"]}, {"table": "pharmasales", "columns": ["sustainable_practice", "latitude", "wrestler_id", "consumption"]}], "writes": [{"table": "faculty", "columns": ["sustainable_practice", "latitude", "wrestler_id", "consumption"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT role_name, workoutdate FROM economic_diversification_argentina LIMIT 201\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO hotel_chains SELECT hireid, partid, attribute_id FROM cuisine WHERE hireid > 364\")\n", "labels": {"reads": [{"table": "economic_diversification_argentina", "columns": ["role_name", "workoutdate"]}, {"table": "cuisine", "columns": ["hireid", "partid", "attribute_id"]}], "writes": [{"table": "hotel_chains", "columns": ["hireid", "partid", "attribute_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table payments --columns song_year,savings --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "payments", "columns": ["song_year", "savings"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT impactid, start FROM view_unit_status LIMIT 68\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "view_unit_status", "columns": ["impactid", "start"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT publicationid, dish FROM auto_show\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"landfill_capacity_north_america\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "auto_show", "columns": ["publicationid", "dish"]}], "writes": [{"table": "landfill_capacity_north_america", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.stg_exposure_daily --columns experiment_name,source_system_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_exposure_daily", "columns": ["experiment_name", "source_system_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO rebounds SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO policyadvocacyevents SELECT a.building_manager, b.transact_date FROM shrimp_farms a JOIN makeup_sales b ON a.organization_details = b.organization_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": null}, {"table": "makeup_sales", "columns": null}], "writes": [{"table": "policyadvocacyevents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO haircaresales SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO permian_basin SELECT a.delivery_time, b.coach_name FROM platform_production a JOIN participation b ON a.posted_at = b.posted_at\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "platform_production", "columns": null}, {"table": "participation", "columns": null}], "writes": [{"table": "permian_basin", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.grant_id > 48).all()\n# src table: province.human_rights_data\nengine.execute(\"INSERT INTO thefts SELECT * FROM province.human_rights_data\")\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": null}], "writes": [{"table": "thefts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM coralreefs\", conn)\ndf.to_sql(\"equipmentsales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "coralreefs", "columns": null}], "writes": [{"table": "equipmentsales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT period, recipient FROM routes LIMIT 4\")\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO record SELECT unit_name, permit_date, meal_name, investorgender FROM excavation_sites WHERE unit_name > 1\")\n", "labels": {"reads": [{"table": "routes", "columns": ["period", "recipient"]}, {"table": "excavation_sites", "columns": ["unit_name", "permit_date", "meal_name", "investorgender"]}], "writes": [{"table": "record", "columns": ["unit_name", "permit_date", "meal_name", "investorgender"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_risk_score\").toPandas()\ndf[[\"workshop_name\", \"home_team_id\"]].to_sql(\"electricvehicles\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": null}], "writes": [{"table": "electricvehicles", "columns": ["workshop_name", "home_team_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT zone_id, observation_date FROM fabricinventory\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"guests\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fabricinventory", "columns": ["zone_id", "observation_date"]}], "writes": [{"table": "guests", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artsales (date_in_locaton_to, tripid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artsales", "columns": ["date_in_locaton_to", "tripid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cropname, emission_date FROM project\", engine)\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"chemical_production_3\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "project", "columns": ["cropname", "emission_date"]}], "writes": [{"table": "chemical_production_3", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.bi_events_daily\")\nsrc.write.insertInto(\"store\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": [{"table": "store", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT amount_outstanding, to_address FROM individual LIMIT 387\")\nimport logging\nspark.sql(\"INSERT INTO animal_budget SELECT order_status_code, temporary_acting FROM contract_transactions WHERE order_status_code > 23\")\n", "labels": {"reads": [{"table": "individual", "columns": ["amount_outstanding", "to_address"]}, {"table": "contract_transactions", "columns": ["order_status_code", "temporary_acting"]}], "writes": [{"table": "animal_budget", "columns": ["order_status_code", "temporary_acting"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contractor_name > 187).all()\n# src table: diversity\nengine.execute(\"INSERT INTO volume SELECT * FROM diversity\")\n", "labels": {"reads": [{"table": "diversity", "columns": null}], "writes": [{"table": "volume", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"test_drives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "test_drives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donations_insert_2\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO genderdistribution SELECT schedule_id, price, neighborhoodid, address_line_2 FROM campaigns WHERE schedule_id > 142\"\n", "labels": {"reads": [{"table": "campaigns", "columns": ["schedule_id", "price", "neighborhoodid", "address_line_2"]}], "writes": [{"table": "genderdistribution", "columns": ["schedule_id", "price", "neighborhoodid", "address_line_2"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM user\"\n", "labels": {"reads": [{"table": "user", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT country, machine FROM fare_collection LIMIT 410\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["country", "machine"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"claims_documents\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"hotel_reviews\")\n", "labels": {"reads": [{"table": "claims_documents", "columns": null}], "writes": [{"table": "hotel_reviews", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO chemical_production_3 SELECT affirmative, creationyear FROM trip WHERE affirmative > 323;\nEOF\n", "labels": {"reads": [{"table": "trip", "columns": ["affirmative", "creationyear"]}], "writes": [{"table": "chemical_production_3", "columns": ["affirmative", "creationyear"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO underwater_cables (mine_type, task_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "underwater_cables", "columns": ["mine_type", "task_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO music_festival SELECT a.composer, b.funding_id FROM traditionalarts a JOIN hotel_business_partnerships b ON a.tree_id = b.tree_id\"\n", "labels": {"reads": [{"table": "traditionalarts", "columns": null}, {"table": "hotel_business_partnerships", "columns": null}], "writes": [{"table": "music_festival", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM cybersecurityincidents\"\n", "labels": {"reads": [{"table": "cybersecurityincidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO carbon_footprint SELECT * FROM legacy\ncur.execute(\"SELECT orderdate, researcher_id FROM refugee_support LIMIT 110\")\n", "labels": {"reads": [{"table": "refugee_support", "columns": ["orderdate", "researcher_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO hosting_city SELECT white, end_date, field_name, savings FROM renewables.renewable_projects WHERE white > 167;\nEOF\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": ["white", "end_date", "field_name", "savings"]}], "writes": [{"table": "hosting_city", "columns": ["white", "end_date", "field_name", "savings"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO product_reviews SELECT a.competition_type, b.number_of_matches FROM problem_log a JOIN influencers b ON a.signupdate = b.signupdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "problem_log", "columns": null}, {"table": "influencers", "columns": null}], "writes": [{"table": "product_reviews", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT customer_code, brandid FROM marine_life_sightings LIMIT 73;\nINSERT INTO fairtradecertifications SELECT accreditation_type, field_name, claim_status_name FROM bi.bi_events_df WHERE accreditation_type > 108;\nSQL\n", "labels": {"reads": [{"table": "marine_life_sightings", "columns": ["customer_code", "brandid"]}, {"table": "bi.bi_events_df", "columns": ["accreditation_type", "field_name", "claim_status_name"]}], "writes": [{"table": "fairtradecertifications", "columns": ["accreditation_type", "field_name", "claim_status_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO sites SELECT a.camera_lens_id, b.appointmentid FROM ads.risk_score a JOIN courtcases b ON a.received_date = b.received_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": null}, {"table": "courtcases", "columns": null}], "writes": [{"table": "sites", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT catalog_id, host FROM streams\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"market\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "streams", "columns": ["catalog_id", "host"]}], "writes": [{"table": "market", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cities\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cities", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO carbon_offsets SELECT a.delivery_time, b.points_per_game FROM injury_accident a JOIN highways b ON a.price = b.price\"\n", "labels": {"reads": [{"table": "injury_accident", "columns": null}, {"table": "highways", "columns": null}], "writes": [{"table": "carbon_offsets", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO broadband_subscribers SELECT * FROM legacy\ncur.execute(\"SELECT mappingname, official_native_language FROM bi.bi_orders_delta LIMIT 385\")\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": ["mappingname", "official_native_language"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"daily_oil_production\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table satellites --target-dir /tmp/land\n", "labels": {"reads": [{"table": "satellites", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO space_exploration (trainingyear, ngo_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "space_exploration", "columns": ["trainingyear", "ngo_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO musical SELECT routeid, staff_id, institution FROM satellites_by_country WHERE routeid > 196\"\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": ["routeid", "staff_id", "institution"]}], "writes": [{"table": "musical", "columns": ["routeid", "staff_id", "institution"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO irrigation_systems SELECT maintenance_id, anomaly, main_industry, department_name FROM dwd_payments_delta WHERE maintenance_id > 273\"\n", "labels": {"reads": [{"table": "dwd_payments_delta", "columns": ["maintenance_id", "anomaly", "main_industry", "department_name"]}], "writes": [{"table": "irrigation_systems", "columns": ["maintenance_id", "anomaly", "main_industry", "department_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO military_personnel_africa (quantity_sold, loadingend) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "military_personnel_africa", "columns": ["quantity_sold", "loadingend"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tokyo_motor_show SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart_events_full SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workout\"\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vesselfuel SELECT a.algorithm_name, b.number_of_hosts FROM inmates a JOIN purchases b ON a.news_story_id = b.news_story_id\"\n", "labels": {"reads": [{"table": "inmates", "columns": null}, {"table": "purchases", "columns": null}], "writes": [{"table": "vesselfuel", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO ticket_sales SELECT clubdesc, region_id, advocate_name FROM open_data_initiatives WHERE clubdesc > 56\"\n", "labels": {"reads": [{"table": "open_data_initiatives", "columns": ["clubdesc", "region_id", "advocate_name"]}], "writes": [{"table": "ticket_sales", "columns": ["clubdesc", "region_id", "advocate_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_products_df SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO excavation SELECT a.culture, b.ihsaa_football_class FROM dws.dws_risk_score_df a JOIN threats b ON a.worker_name = b.worker_name\"\n", "labels": {"reads": [{"table": "dws.dws_risk_score_df", "columns": null}, {"table": "threats", "columns": null}], "writes": [{"table": "excavation", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO species_observations SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.stg_events_di SELECT operation, donor_country FROM ads.sessions_hourly WHERE operation > 7;\nEOF\n", "labels": {"reads": [{"table": "ads.sessions_hourly", "columns": ["operation", "donor_country"]}], "writes": [{"table": "stg.stg_events_di", "columns": ["operation", "donor_country"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.mart_payments_df SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table sustainable_tourism_practices --columns active_to_date,plant --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "sustainable_tourism_practices", "columns": ["active_to_date", "plant"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO ca_menu_items SELECT length_meters, environmental_impact, school_id FROM philadelphia_police_emergencies WHERE length_meters > 3\")\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": ["length_meters", "environmental_impact", "school_id"]}], "writes": [{"table": "ca_menu_items", "columns": ["length_meters", "environmental_impact", "school_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recovery_program SELECT * FROM legacy\ncur.execute(\"SELECT competition, postalcode FROM mart.mart_device_log LIMIT 231\")\n", "labels": {"reads": [{"table": "mart.mart_device_log", "columns": ["competition", "postalcode"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"labour_productivity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_orders_daily\")\n", "labels": {"reads": [{"table": "labour_productivity", "columns": null}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"market\")\nsrc.write.insertInto(\"experience\", overwrite=True)\n", "labels": {"reads": [{"table": "market", "columns": null}], "writes": [{"table": "experience", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO militarydrones SELECT area_name, donationid FROM opioid_overdoses WHERE area_name > 125\")\n", "labels": {"reads": [{"table": "opioid_overdoses", "columns": ["area_name", "donationid"]}], "writes": [{"table": "militarydrones", "columns": ["area_name", "donationid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artifacts SELECT last_maintenance, delegate, rainfall FROM ai_systems WHERE last_maintenance > 154\"\n", "labels": {"reads": [{"table": "ai_systems", "columns": ["last_maintenance", "delegate", "rainfall"]}], "writes": [{"table": "artifacts", "columns": ["last_maintenance", "delegate", "rainfall"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM state_info\", conn)\ndf.to_sql(\"cultural_competency\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "state_info", "columns": null}], "writes": [{"table": "cultural_competency", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table online_platform --columns period,grape --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "online_platform", "columns": ["period", "grape"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table intelligence_personnel --columns sitename,description --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "intelligence_personnel", "columns": ["sitename", "description"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO stg.device_log_df SELECT fleet_id, num_owners FROM mining_operations WHERE fleet_id > 231\"\n", "labels": {"reads": [{"table": "mining_operations", "columns": ["fleet_id", "num_owners"]}], "writes": [{"table": "stg.device_log_df", "columns": ["fleet_id", "num_owners"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rent_arrears\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"landfillcapacitybycountry\")\n", "labels": {"reads": [{"table": "rent_arrears", "columns": null}], "writes": [{"table": "landfillcapacitybycountry", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"e_scooter_trips\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"aquatic_farms\")\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": null}], "writes": [{"table": "aquatic_farms", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO vessel_types SELECT a.round_date, b.state_county FROM safe_dataset a JOIN dailystreams b ON a.working_horses = b.working_horses\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "safe_dataset", "columns": null}, {"table": "dailystreams", "columns": null}], "writes": [{"table": "vessel_types", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO artist_concerts SELECT virtual_tour_engagement_time, style FROM stg.stg_campaigns_hourly WHERE virtual_tour_engagement_time > 433;\nEOF\n", "labels": {"reads": [{"table": "stg.stg_campaigns_hourly", "columns": ["virtual_tour_engagement_time", "style"]}], "writes": [{"table": "artist_concerts", "columns": ["virtual_tour_engagement_time", "style"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO economic_diversification_efforts SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.mart_users_delta\", conn)\ndf.to_sql(\"tracks\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_users_delta", "columns": null}], "writes": [{"table": "tracks", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO shipmentinfo SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"operation\")\nupsert_to_target(df, \"climate_finance_asia\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "operation", "columns": null}], "writes": [{"table": "climate_finance_asia", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO manufacturersustainability SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO staff_department_assignments SELECT claim_number, distributorid FROM fraud_detections WHERE claim_number > 307\"\n", "labels": {"reads": [{"table": "fraud_detections", "columns": ["claim_number", "distributorid"]}], "writes": [{"table": "staff_department_assignments", "columns": ["claim_number", "distributorid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_payments_delta SELECT gender_mf, dish, gender_group FROM safety_incident WHERE gender_mf > 438\"], check=True)\n", "labels": {"reads": [{"table": "safety_incident", "columns": ["gender_mf", "dish", "gender_group"]}], "writes": [{"table": "bi.bi_payments_delta", "columns": ["gender_mf", "dish", "gender_group"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT goals, initiativename FROM section LIMIT 93\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "section", "columns": ["goals", "initiativename"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO labor_practices SELECT num_of_factories, staff_last_name FROM canals WHERE num_of_factories > 115\"\n", "labels": {"reads": [{"table": "canals", "columns": ["num_of_factories", "staff_last_name"]}], "writes": [{"table": "labor_practices", "columns": ["num_of_factories", "staff_last_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"benefits_overpayments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO buildings SELECT daily_sales, part_id FROM experience WHERE daily_sales > 469\"\n", "labels": {"reads": [{"table": "experience", "columns": ["daily_sales", "part_id"]}], "writes": [{"table": "buildings", "columns": ["daily_sales", "part_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_orders_daily\").toPandas()\ndf[[\"songid\", \"departure_date\"]].to_sql(\"sponsor_trials\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": null}], "writes": [{"table": "sponsor_trials", "columns": ["songid", "departure_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table crops_table --columns followers,species_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "crops_table", "columns": ["followers", "species_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.away_team > 453).all()\n# src table: volunteer_registration\nengine.execute(\"INSERT INTO ecohousing SELECT * FROM volunteer_registration\")\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": [{"table": "ecohousing", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO faculty SELECT fish_count, brand_name FROM program_budget WHERE fish_count > 67;\nEOF\n", "labels": {"reads": [{"table": "program_budget", "columns": ["fish_count", "brand_name"]}], "writes": [{"table": "faculty", "columns": ["fish_count", "brand_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO seafood SELECT commanding_officer, membership_amount, era, denomination FROM enzyme WHERE commanding_officer > 308\"\n", "labels": {"reads": [{"table": "enzyme", "columns": ["commanding_officer", "membership_amount", "era", "denomination"]}], "writes": [{"table": "seafood", "columns": ["commanding_officer", "membership_amount", "era", "denomination"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_incident\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"asia_events\")\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}], "writes": [{"table": "asia_events", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO coal_reserves SELECT business_name, characteristic_type_code, issue_month, fuelid FROM fault_log_parts WHERE business_name > 353\"\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": ["business_name", "characteristic_type_code", "issue_month", "fuelid"]}], "writes": [{"table": "coal_reserves", "columns": ["business_name", "characteristic_type_code", "issue_month", "fuelid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO nomination SELECT market_rate, compatible_since_year, acidification_level, mid FROM research_grants WHERE market_rate > 303\"\n", "labels": {"reads": [{"table": "research_grants", "columns": ["market_rate", "compatible_since_year", "acidification_level", "mid"]}], "writes": [{"table": "nomination", "columns": ["market_rate", "compatible_since_year", "acidification_level", "mid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO community_development.transactions SELECT art_id, customer_event_id, share_count, order_shipping_charges FROM video_games WHERE art_id > 246\"\n", "labels": {"reads": [{"table": "video_games", "columns": ["art_id", "customer_event_id", "share_count", "order_shipping_charges"]}], "writes": [{"table": "community_development.transactions", "columns": ["art_id", "customer_event_id", "share_count", "order_shipping_charges"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT restypename, court_appearances FROM circulation_history LIMIT 136;\nINSERT INTO videos SELECT development_name, undergraduate, amount_paid FROM mart.mart_risk_score_hourly WHERE development_name > 18;\nSQL\n", "labels": {"reads": [{"table": "circulation_history", "columns": ["restypename", "court_appearances"]}, {"table": "mart.mart_risk_score_hourly", "columns": ["development_name", "undergraduate", "amount_paid"]}], "writes": [{"table": "videos", "columns": ["development_name", "undergraduate", "amount_paid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO culturalevents SELECT num_attendees, green_building_id, min_salary FROM refugees WHERE num_attendees > 103\"\n", "labels": {"reads": [{"table": "refugees", "columns": ["num_attendees", "green_building_id", "min_salary"]}], "writes": [{"table": "culturalevents", "columns": ["num_attendees", "green_building_id", "min_salary"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO advisor SELECT organizationid, produceid, directed_by, roomtype FROM flight_emissions WHERE organizationid > 370\"\n", "labels": {"reads": [{"table": "flight_emissions", "columns": ["organizationid", "produceid", "directed_by", "roomtype"]}], "writes": [{"table": "advisor", "columns": ["organizationid", "produceid", "directed_by", "roomtype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO galleries SELECT energy_id, membergender, cases_count FROM military_personnel_africa WHERE energy_id > 406\"], check=True)\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": ["energy_id", "membergender", "cases_count"]}], "writes": [{"table": "galleries", "columns": ["energy_id", "membergender", "cases_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safetytests\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"crime_stats\")\n", "labels": {"reads": [{"table": "safetytests", "columns": null}], "writes": [{"table": "crime_stats", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO milestones SELECT secretary_vote, asset_acquired_date, capacity, research_name FROM gymnast WHERE secretary_vote > 157\"\n", "labels": {"reads": [{"table": "gymnast", "columns": ["secretary_vote", "asset_acquired_date", "capacity", "research_name"]}], "writes": [{"table": "milestones", "columns": ["secretary_vote", "asset_acquired_date", "capacity", "research_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT garment_type, sustainability_certified FROM gamedesign LIMIT 19\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO topublictransportation SELECT route, claim_id, thing_id FROM user_genre WHERE route > 226\")\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["garment_type", "sustainability_certified"]}, {"table": "user_genre", "columns": ["route", "claim_id", "thing_id"]}], "writes": [{"table": "topublictransportation", "columns": ["route", "claim_id", "thing_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM species_observations\"\n", "labels": {"reads": [{"table": "species_observations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sex, threat FROM dw_orders_df\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"farmers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dw_orders_df", "columns": ["sex", "threat"]}], "writes": [{"table": "farmers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO provider_training SELECT sales_id, workforce_development, domestic_passengers, platformid FROM safety_data WHERE sales_id > 218\"\n", "labels": {"reads": [{"table": "safety_data", "columns": ["sales_id", "workforce_development", "domestic_passengers", "platformid"]}], "writes": [{"table": "provider_training", "columns": ["sales_id", "workforce_development", "domestic_passengers", "platformid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO immunization (stream_id, amount_funded) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "immunization", "columns": ["stream_id", "amount_funded"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM government_transparency\", conn)\ndf.to_sql(\"docking\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "government_transparency", "columns": null}], "writes": [{"table": "docking", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws_clicks_di SELECT age_group, rate, pediatrician_id FROM concentrateprices WHERE age_group > 296\"], check=True)\n", "labels": {"reads": [{"table": "concentrateprices", "columns": ["age_group", "rate", "pediatrician_id"]}], "writes": [{"table": "dws_clicks_di", "columns": ["age_group", "rate", "pediatrician_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table studentaccommodations --columns organization_id,founder_ethnicity --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "studentaccommodations", "columns": ["organization_id", "founder_ethnicity"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO renewable_projects (fair_trade, supplier_country) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "renewable_projects", "columns": ["fair_trade", "supplier_country"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO party SELECT driver_id, stationid, to_address FROM skincaresales WHERE driver_id > 431\")\n", "labels": {"reads": [{"table": "skincaresales", "columns": ["driver_id", "stationid", "to_address"]}], "writes": [{"table": "party", "columns": ["driver_id", "stationid", "to_address"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO patient SELECT membership_card, round FROM document_sections_images WHERE membership_card > 325\"\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["membership_card", "round"]}], "writes": [{"table": "patient", "columns": ["membership_card", "round"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecurity_incidents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cybersecurity_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.opened_date > 363).all()\n# src table: policy_feedback\nengine.execute(\"INSERT INTO education_union SELECT * FROM policy_feedback\")\n", "labels": {"reads": [{"table": "policy_feedback", "columns": null}], "writes": [{"table": "education_union", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO co2_sequestration SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO defense_contracts SELECT developer_id, class_section, game_genre FROM tickets WHERE developer_id > 26\"\n", "labels": {"reads": [{"table": "tickets", "columns": ["developer_id", "class_section", "game_genre"]}], "writes": [{"table": "defense_contracts", "columns": ["developer_id", "class_section", "game_genre"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT exhibition_id, donator_name FROM fraud_detections LIMIT 176\")\nlogger = logging.getLogger(__name__)\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO highest_scores SELECT bathroom_count, albumid FROM habitat WHERE bathroom_count > 202\")\n", "labels": {"reads": [{"table": "fraud_detections", "columns": ["exhibition_id", "donator_name"]}, {"table": "habitat", "columns": ["bathroom_count", "albumid"]}], "writes": [{"table": "highest_scores", "columns": ["bathroom_count", "albumid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mine (gamepreference, program) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mine", "columns": ["gamepreference", "program"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO packages SELECT a.hoursspent, b.part_name FROM australian_states a JOIN lessons b ON a.brandid = b.brandid\"\n", "labels": {"reads": [{"table": "australian_states", "columns": null}, {"table": "lessons", "columns": null}], "writes": [{"table": "packages", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"carbon_offset_programs\")\nsrc.write.insertInto(\"providers\", overwrite=True)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}], "writes": [{"table": "providers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO album SELECT monthly_rental, reported FROM beverages WHERE monthly_rental > 269;\nEOF\n", "labels": {"reads": [{"table": "beverages", "columns": ["monthly_rental", "reported"]}], "writes": [{"table": "album", "columns": ["monthly_rental", "reported"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table researchpapers --columns region_name,export_country --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "researchpapers", "columns": ["region_name", "export_country"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"providers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"species_forests\")\n", "labels": {"reads": [{"table": "providers", "columns": null}], "writes": [{"table": "species_forests", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model recyclers depends on traffic_accidents\ndbt run --select recyclers --vars '{\"source_table\":\"traffic_accidents\"}'\n", "labels": {"reads": [{"table": "traffic_accidents", "columns": null}], "writes": [{"table": "recyclers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"donor\")\npush_to_target(df, \"conservation_initiatives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "donor", "columns": null}], "writes": [{"table": "conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO biosensor.patents SELECT src_apid, membership FROM waterconservation WHERE src_apid > 453\"\n", "labels": {"reads": [{"table": "waterconservation", "columns": ["src_apid", "membership"]}], "writes": [{"table": "biosensor.patents", "columns": ["src_apid", "membership"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO socially_responsible_lending SELECT a.contact_staff_id, b.first_donation_date FROM province.human_rights_data a JOIN food_items b ON a.seating = b.seating\"\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": null}, {"table": "food_items", "columns": null}], "writes": [{"table": "socially_responsible_lending", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO publications SELECT gross_worldwide, hours FROM team WHERE gross_worldwide > 400;\nEOF\n", "labels": {"reads": [{"table": "team", "columns": ["gross_worldwide", "hours"]}], "writes": [{"table": "publications", "columns": ["gross_worldwide", "hours"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd.dwd_products SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT document_id, dish_id FROM mart.campaigns_di LIMIT 294\")\nresult = value * ratio + offset\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO cloud_issues SELECT cases_handled, age_group_id, post_id FROM gymnast WHERE cases_handled > 333\")\n", "labels": {"reads": [{"table": "mart.campaigns_di", "columns": ["document_id", "dish_id"]}, {"table": "gymnast", "columns": ["cases_handled", "age_group_id", "post_id"]}], "writes": [{"table": "cloud_issues", "columns": ["cases_handled", "age_group_id", "post_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_campaigns_hourly SELECT pilot, group_equity_shareholding FROM supportservices WHERE pilot > 500\"], check=True)\n", "labels": {"reads": [{"table": "supportservices", "columns": ["pilot", "group_equity_shareholding"]}], "writes": [{"table": "stg.stg_campaigns_hourly", "columns": ["pilot", "group_equity_shareholding"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT water_depth, building_phone FROM endowment LIMIT 437\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO stg.stg_exposure_di SELECT contract_start_date, firstdonationdate, energy_generated, news_outlet FROM exhibition_visits WHERE contract_start_date > 411\")\n", "labels": {"reads": [{"table": "endowment", "columns": ["water_depth", "building_phone"]}, {"table": "exhibition_visits", "columns": ["contract_start_date", "firstdonationdate", "energy_generated", "news_outlet"]}], "writes": [{"table": "stg.stg_exposure_di", "columns": ["contract_start_date", "firstdonationdate", "energy_generated", "news_outlet"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ocean_acidification --columns languageid,ship_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ocean_acidification", "columns": ["languageid", "ship_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.stg_coupon_use_di SELECT effective_date, catalog_level_number, event_attendance, call_id FROM labor_hours WHERE effective_date > 272;\nEOF\n", "labels": {"reads": [{"table": "labor_hours", "columns": ["effective_date", "catalog_level_number", "event_attendance", "call_id"]}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["effective_date", "catalog_level_number", "event_attendance", "call_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO circular_supply_chain_products (quantityproduced, building_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "circular_supply_chain_products", "columns": ["quantityproduced", "building_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO restaurants_tx (destination_id, ram_mib) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "restaurants_tx", "columns": ["destination_id", "ram_mib"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"innovation_metrics\").toPandas()\ndf[[\"zipcode\", \"country1\"]].to_sql(\"biotech.startups\", engine, index=False)\n", "labels": {"reads": [{"table": "innovation_metrics", "columns": null}], "writes": [{"table": "biotech.startups", "columns": ["zipcode", "country1"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO disabilitysupportprograms (value, course_description) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "disabilitysupportprograms", "columns": ["value", "course_description"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"instructor\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"co_ownership\")\n", "labels": {"reads": [{"table": "instructor", "columns": null}], "writes": [{"table": "co_ownership", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"climate_finance_organizations\")\nsink_to_target(df, \"spacecraft_manufacturers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": [{"table": "spacecraft_manufacturers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table first_notification_of_loss --columns calendar_date,laborproductivity --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "first_notification_of_loss", "columns": ["calendar_date", "laborproductivity"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"instructor\")\nsrc.write.insertInto(\"us_cities\", overwrite=True)\n", "labels": {"reads": [{"table": "instructor", "columns": null}], "writes": [{"table": "us_cities", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM auctions\"\n", "labels": {"reads": [{"table": "auctions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO chemicalproducts SELECT document_type_code, rating_in_percent, item_id FROM trade_history WHERE document_type_code > 260\"], check=True)\n", "labels": {"reads": [{"table": "trade_history", "columns": ["document_type_code", "rating_in_percent", "item_id"]}], "writes": [{"table": "chemicalproducts", "columns": ["document_type_code", "rating_in_percent", "item_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ref_incident_type\"\n", "labels": {"reads": [{"table": "ref_incident_type", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sponsor_trials SELECT snatch, degrees FROM station_company WHERE snatch > 322;\nEOF\n", "labels": {"reads": [{"table": "station_company", "columns": ["snatch", "degrees"]}], "writes": [{"table": "sponsor_trials", "columns": ["snatch", "degrees"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT claim_amount, journalist_id FROM publicchargingstations LIMIT 341;\nINSERT INTO countries SELECT year_working, race_ethnicity_id, train_id FROM county_public_safety WHERE year_working > 74;\nSQL\n", "labels": {"reads": [{"table": "publicchargingstations", "columns": ["claim_amount", "journalist_id"]}, {"table": "county_public_safety", "columns": ["year_working", "race_ethnicity_id", "train_id"]}], "writes": [{"table": "countries", "columns": ["year_working", "race_ethnicity_id", "train_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.course_name > 221).all()\n# src table: roads\nengine.execute(\"INSERT INTO culturalevents SELECT * FROM roads\")\n", "labels": {"reads": [{"table": "roads", "columns": null}], "writes": [{"table": "culturalevents", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_payments_delta\").toPandas()\ndf[[\"party_phone\", \"content_type\"]].to_sql(\"billstatus\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": null}], "writes": [{"table": "billstatus", "columns": ["party_phone", "content_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table rd_expenditure --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO financialwellbeing SELECT a.made_in_usa, b.investors FROM platformstats a JOIN dw.dw_users_di b ON a.artist_name = b.artist_name\"\n", "labels": {"reads": [{"table": "platformstats", "columns": null}, {"table": "dw.dw_users_di", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO safetytestingcounts SELECT hospitalid, date_payment_made, review_text FROM bi.bi_events_full WHERE hospitalid > 485\"\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": ["hospitalid", "date_payment_made", "review_text"]}], "writes": [{"table": "safetytestingcounts", "columns": ["hospitalid", "date_payment_made", "review_text"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO vehicle_registrations SELECT sustainability_initiative_id, brandname, protein_name, carbon_footprint FROM transportation_per_country WHERE sustainability_initiative_id > 431\"\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": ["sustainability_initiative_id", "brandname", "protein_name", "carbon_footprint"]}], "writes": [{"table": "vehicle_registrations", "columns": ["sustainability_initiative_id", "brandname", "protein_name", "carbon_footprint"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO waterconservation SELECT statement_id, num_rooms FROM wedding WHERE statement_id > 438\")\n", "labels": {"reads": [{"table": "wedding", "columns": ["statement_id", "num_rooms"]}], "writes": [{"table": "waterconservation", "columns": ["statement_id", "num_rooms"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversification_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"lenders\")\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dw.dw_orders_hourly SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO catalogs SELECT * FROM legacy\ncur.execute(\"SELECT gold, review_rating FROM bike_share LIMIT 451\")\n", "labels": {"reads": [{"table": "bike_share", "columns": ["gold", "review_rating"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"minor_in\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ref_budget_codes\")\n", "labels": {"reads": [{"table": "minor_in", "columns": null}], "writes": [{"table": "ref_budget_codes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO daily_transaction_volume SELECT injury, hiv, ship_date, vesselname FROM dwd.dwd_users_hourly WHERE injury > 230\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_users_hourly", "columns": ["injury", "hiv", "ship_date", "vesselname"]}], "writes": [{"table": "daily_transaction_volume", "columns": ["injury", "hiv", "ship_date", "vesselname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table hotel_business_partnerships --columns certification_id,system --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "hotel_business_partnerships", "columns": ["certification_id", "system"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model labour_productivity depends on pets\ndbt run --select labour_productivity --vars '{\"src\":\"pets\"}'\n", "labels": {"reads": [{"table": "pets", "columns": null}], "writes": [{"table": "labour_productivity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM development_hours\", conn)\ndf.to_sql(\"sportsinfo\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "development_hours", "columns": null}], "writes": [{"table": "sportsinfo", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO program_history SELECT astronaut_name, competition_type FROM ods.member_point_df WHERE astronaut_name > 442\"\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": ["astronaut_name", "competition_type"]}], "writes": [{"table": "program_history", "columns": ["astronaut_name", "competition_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO regional_archaeologists SELECT start_date, main_industry FROM state_info WHERE start_date > 382;\nEOF\n", "labels": {"reads": [{"table": "state_info", "columns": ["start_date", "main_industry"]}], "writes": [{"table": "regional_archaeologists", "columns": ["start_date", "main_industry"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_shipments_delta SELECT aid_name, asset_name, founded_year, offender_name FROM heritagesites WHERE aid_name > 177\"\n", "labels": {"reads": [{"table": "heritagesites", "columns": ["aid_name", "asset_name", "founded_year", "offender_name"]}], "writes": [{"table": "ads.ads_shipments_delta", "columns": ["aid_name", "asset_name", "founded_year", "offender_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table voting_data --columns vessel,student_capacity --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "voting_data", "columns": ["vessel", "student_capacity"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"esa_missions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"inspectiondata\")\n", "labels": {"reads": [{"table": "esa_missions", "columns": null}], "writes": [{"table": "inspectiondata", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO district_schools SELECT a.gamesplayed, b.haslegalprecedent FROM collective_bargaining a JOIN electronics_factories b ON a.org_id = b.org_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "collective_bargaining", "columns": null}, {"table": "electronics_factories", "columns": null}], "writes": [{"table": "district_schools", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"co_ownership_program\")\nsave_to_sink(df, \"stg.stg_inventory_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "co_ownership_program", "columns": null}], "writes": [{"table": "stg.stg_inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamegenres\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"drug_approvals\")\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": [{"table": "drug_approvals", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT customer_address, restaurant_name FROM news_views LIMIT 265;\nINSERT INTO machine_emissions SELECT prominence, health_equity_metric_2, stu_gpa FROM laptimes WHERE prominence > 323;\nSQL\n", "labels": {"reads": [{"table": "news_views", "columns": ["customer_address", "restaurant_name"]}, {"table": "laptimes", "columns": ["prominence", "health_equity_metric_2", "stu_gpa"]}], "writes": [{"table": "machine_emissions", "columns": ["prominence", "health_equity_metric_2", "stu_gpa"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table dws.exposure_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.exposure_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO climate_finance_re SELECT rig_id, winery, launch_date FROM watertreatmentplants WHERE rig_id > 256\"\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["rig_id", "winery", "launch_date"]}], "writes": [{"table": "climate_finance_re", "columns": ["rig_id", "winery", "launch_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mars_spacecraft SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ai_id, prereq_id FROM drug_sales\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ocean_acidification\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "drug_sales", "columns": ["ai_id", "prereq_id"]}], "writes": [{"table": "ocean_acidification", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.shipments_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"coowners\")\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO water_conservation_brazil SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT program_category, ngo_name FROM innovation_trends LIMIT 166\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "innovation_trends", "columns": ["program_category", "ngo_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO province.human_rights_data SELECT cultivatorname, org_size, testtypeid FROM precipitation_data WHERE cultivatorname > 78\")\n", "labels": {"reads": [{"table": "precipitation_data", "columns": ["cultivatorname", "org_size", "testtypeid"]}], "writes": [{"table": "province.human_rights_data", "columns": ["cultivatorname", "org_size", "testtypeid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"fair_trade_suppliers\")\nsrc.write.insertInto(\"individual\", overwrite=True)\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": null}], "writes": [{"table": "individual", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO programoutcomes SELECT a.shipmenttype, b.gross_worldwide FROM life_expectancy a JOIN sustainabilityratings b ON a.has_aloe_vera = b.has_aloe_vera\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "life_expectancy", "columns": null}, {"table": "sustainabilityratings", "columns": null}], "writes": [{"table": "programoutcomes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainabilityratings SELECT song_year, count_time, workers FROM overwatch_scores WHERE song_year > 302\"\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": ["song_year", "count_time", "workers"]}], "writes": [{"table": "sustainabilityratings", "columns": ["song_year", "count_time", "workers"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_stats\").toPandas()\ndf[[\"duration\", \"preference_score\"]].to_sql(\"sustainable_tourism_practices\", engine, index=False)\n", "labels": {"reads": [{"table": "user_stats", "columns": null}], "writes": [{"table": "sustainable_tourism_practices", "columns": ["duration", "preference_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT network, attorney_id FROM intelligenceoperations LIMIT 271;\nINSERT INTO dws.dws_events_df SELECT mentalhealthscore, season FROM industrial_building_energy_efficiency WHERE mentalhealthscore > 452;\nSQL\n", "labels": {"reads": [{"table": "intelligenceoperations", "columns": ["network", "attorney_id"]}, {"table": "industrial_building_energy_efficiency", "columns": ["mentalhealthscore", "season"]}], "writes": [{"table": "dws.dws_events_df", "columns": ["mentalhealthscore", "season"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.provider_parity_score > 349).all()\n# src table: recreation_centers\nengine.execute(\"INSERT INTO gamedata SELECT * FROM recreation_centers\")\n", "labels": {"reads": [{"table": "recreation_centers", "columns": null}], "writes": [{"table": "gamedata", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fossil_fuel_vehicles_japan SELECT pages_per_minute_color, is_organic, wind_speed_mph, practicename FROM sustainable_urban_properties_2 WHERE pages_per_minute_color > 96\"\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": ["pages_per_minute_color", "is_organic", "wind_speed_mph", "practicename"]}], "writes": [{"table": "fossil_fuel_vehicles_japan", "columns": ["pages_per_minute_color", "is_organic", "wind_speed_mph", "practicename"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_pd_hours\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"regional_archaeologists\")\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": null}], "writes": [{"table": "regional_archaeologists", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mobile_usage --columns video_id,matchdate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mobile_usage", "columns": ["video_id", "matchdate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO project_timelines SELECT patient_age, shop_name, total_horses, astronautid FROM yttrium_production WHERE patient_age > 118\"\n", "labels": {"reads": [{"table": "yttrium_production", "columns": ["patient_age", "shop_name", "total_horses", "astronautid"]}], "writes": [{"table": "project_timelines", "columns": ["patient_age", "shop_name", "total_horses", "astronautid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart.mart_coupon_use_delta\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO driver SELECT complaint_id, startyear, stu_dob FROM ref_calendar WHERE complaint_id > 377\"\n", "labels": {"reads": [{"table": "ref_calendar", "columns": ["complaint_id", "startyear", "stu_dob"]}], "writes": [{"table": "driver", "columns": ["complaint_id", "startyear", "stu_dob"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"low_value_contracts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"buildings\")\n", "labels": {"reads": [{"table": "low_value_contracts", "columns": null}], "writes": [{"table": "buildings", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO student_addresses (booking_status_code, merchandise_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "student_addresses", "columns": ["booking_status_code", "merchandise_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO projecttimelinebybudget SELECT a.assessmentname, b.retail_price FROM militaryequipmentsales a JOIN drug_approval b ON a.date_in_locaton_to = b.date_in_locaton_to\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "militaryequipmentsales", "columns": null}, {"table": "drug_approval", "columns": null}], "writes": [{"table": "projecttimelinebybudget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO basketball_teams SELECT reported, product_quantity FROM medicine WHERE reported > 179\"\n", "labels": {"reads": [{"table": "medicine", "columns": ["reported", "product_quantity"]}], "writes": [{"table": "basketball_teams", "columns": ["reported", "product_quantity"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO solana_transactions SELECT a.principal_activities, b.player_name FROM creative_ai a JOIN classroom b ON a.trial_success_rate = b.trial_success_rate\"\n", "labels": {"reads": [{"table": "creative_ai", "columns": null}, {"table": "classroom", "columns": null}], "writes": [{"table": "solana_transactions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT file_size, wheels FROM arcticocean\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"problem_log\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "arcticocean", "columns": ["file_size", "wheels"]}], "writes": [{"table": "problem_log", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO galleryc SELECT customer_id, loadingend, grantamount, number_of_vessels FROM safety_violations WHERE customer_id > 345\"\n", "labels": {"reads": [{"table": "safety_violations", "columns": ["customer_id", "loadingend", "grantamount", "number_of_vessels"]}], "writes": [{"table": "galleryc", "columns": ["customer_id", "loadingend", "grantamount", "number_of_vessels"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT word_count, fastestlapspeed FROM public.trips_by_day_train LIMIT 375;\nINSERT INTO recycledmaterialsgarments SELECT builder, day_of_week, played FROM equipment_sales WHERE builder > 479;\nSQL\n", "labels": {"reads": [{"table": "public.trips_by_day_train", "columns": ["word_count", "fastestlapspeed"]}, {"table": "equipment_sales", "columns": ["builder", "day_of_week", "played"]}], "writes": [{"table": "recycledmaterialsgarments", "columns": ["builder", "day_of_week", "played"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"event_attendance\")\nsrc.write.insertInto(\"military_tech\", overwrite=True)\n", "labels": {"reads": [{"table": "event_attendance", "columns": null}], "writes": [{"table": "military_tech", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vessel_registry SELECT a.cropname, b.report_date FROM user_interests a JOIN textile_suppliers b ON a.delivery_id = b.delivery_id\"\n", "labels": {"reads": [{"table": "user_interests", "columns": null}, {"table": "textile_suppliers", "columns": null}], "writes": [{"table": "vessel_registry", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO incidents SELECT cvid, daily_visitors FROM residents_services WHERE cvid > 161\"\n", "labels": {"reads": [{"table": "residents_services", "columns": ["cvid", "daily_visitors"]}], "writes": [{"table": "incidents", "columns": ["cvid", "daily_visitors"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"disasters\").toPandas()\ndf[[\"injury_date\", \"stadium_id\"]].to_sql(\"chip_model\", engine, index=False)\n", "labels": {"reads": [{"table": "disasters", "columns": null}], "writes": [{"table": "chip_model", "columns": ["injury_date", "stadium_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO aquaticfarm SELECT wage, vessel_id FROM store_product WHERE wage > 141;\nEOF\n", "labels": {"reads": [{"table": "store_product", "columns": ["wage", "vessel_id"]}], "writes": [{"table": "aquaticfarm", "columns": ["wage", "vessel_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.royal_family_id > 249).all()\n# src table: industry_funding\nengine.execute(\"INSERT INTO dwd.dwd_orders_di SELECT * FROM industry_funding\")\n", "labels": {"reads": [{"table": "industry_funding", "columns": null}], "writes": [{"table": "dwd.dwd_orders_di", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO readership SELECT a.galleryid, b.well_name FROM weather a JOIN first_notification_of_loss b ON a.building_address = b.building_address\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "weather", "columns": null}, {"table": "first_notification_of_loss", "columns": null}], "writes": [{"table": "readership", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO biosensors.patents (coach_name, statement_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "biosensors.patents", "columns": ["coach_name", "statement_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO southchinasea.wells SELECT * FROM legacy\ncur.execute(\"SELECT matchdate, membergender FROM mart_shipments_full LIMIT 71\")\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": ["matchdate", "membergender"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"representative\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "representative", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"hockey_players\")\nsink_to_target(df, \"ads.users_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "hockey_players", "columns": null}], "writes": [{"table": "ads.users_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.inventory_delta SELECT art_movement, cb_year, evaluationid FROM educators WHERE art_movement > 226\"], check=True)\n", "labels": {"reads": [{"table": "educators", "columns": ["art_movement", "cb_year", "evaluationid"]}], "writes": [{"table": "bi.inventory_delta", "columns": ["art_movement", "cb_year", "evaluationid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ai_projects SELECT lesson_time, aid_name, trackid, workout_name FROM vrplayers WHERE lesson_time > 234\"], check=True)\n", "labels": {"reads": [{"table": "vrplayers", "columns": ["lesson_time", "aid_name", "trackid", "workout_name"]}], "writes": [{"table": "ai_projects", "columns": ["lesson_time", "aid_name", "trackid", "workout_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fields SELECT business_name, investment_name, dnumber FROM labor_statistics WHERE business_name > 114\"\n", "labels": {"reads": [{"table": "labor_statistics", "columns": ["business_name", "investment_name", "dnumber"]}], "writes": [{"table": "fields", "columns": ["business_name", "investment_name", "dnumber"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"animal_rehab\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fault_log\")\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": [{"table": "fault_log", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO units SELECT a.vaccinations, b.headquarters FROM biosensors.projects a JOIN stg.refunds b ON a.capacity_percentage = b.capacity_percentage\"\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}, {"table": "stg.refunds", "columns": null}], "writes": [{"table": "units", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ship (main_industry, wastetype) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ship", "columns": ["main_industry", "wastetype"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.device_log_hourly (event_id, operation_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.device_log_hourly", "columns": ["event_id", "operation_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO performance_scores SELECT specialty, money_requested, violation_id, artist_id FROM dw.dw_events_di WHERE specialty > 474\"], check=True)\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": ["specialty", "money_requested", "violation_id", "artist_id"]}], "writes": [{"table": "performance_scores", "columns": ["specialty", "money_requested", "violation_id", "artist_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO public.crime_types SELECT health_equity_metric_3, attack_country, recipient, taskdate FROM militarypersonnel WHERE health_equity_metric_3 > 309\"], check=True)\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": ["health_equity_metric_3", "attack_country", "recipient", "taskdate"]}], "writes": [{"table": "public.crime_types", "columns": ["health_equity_metric_3", "attack_country", "recipient", "taskdate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"crime_reports\").toPandas()\ndf[[\"condition\", \"visit_date\"]].to_sql(\"billstatus\", engine, index=False)\n", "labels": {"reads": [{"table": "crime_reports", "columns": null}], "writes": [{"table": "billstatus", "columns": ["condition", "visit_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT party_email, effort FROM ods.ods_payments_full LIMIT 333\")\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO weights SELECT equipment_name, mine_name FROM healthcare_facilities WHERE equipment_name > 192\")\n", "labels": {"reads": [{"table": "ods.ods_payments_full", "columns": ["party_email", "effort"]}, {"table": "healthcare_facilities", "columns": ["equipment_name", "mine_name"]}], "writes": [{"table": "weights", "columns": ["equipment_name", "mine_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO training SELECT initiative_type, playtime, accessible, trade_name FROM electric_buses WHERE initiative_type > 347\")\n", "labels": {"reads": [{"table": "electric_buses", "columns": ["initiative_type", "playtime", "accessible", "trade_name"]}], "writes": [{"table": "training", "columns": ["initiative_type", "playtime", "accessible", "trade_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO sales_2 (order_quantity, offer_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "sales_2", "columns": ["order_quantity", "offer_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tickets SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO marinespeciesobservations SELECT characteristic_data_type, budget_allocated, total_value_purchased FROM faculty WHERE characteristic_data_type > 99\"\n", "labels": {"reads": [{"table": "faculty", "columns": ["characteristic_data_type", "budget_allocated", "total_value_purchased"]}], "writes": [{"table": "marinespeciesobservations", "columns": ["characteristic_data_type", "budget_allocated", "total_value_purchased"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO biosensors.projects SELECT stop, plant, lipstick_id, squadron FROM ocean_acidification WHERE stop > 397\"\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": ["stop", "plant", "lipstick_id", "squadron"]}], "writes": [{"table": "biosensors.projects", "columns": ["stop", "plant", "lipstick_id", "squadron"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO mart.mart_risk_score_hourly SELECT openning_year, invoicedate FROM safety_incidents WHERE openning_year > 311\"\n", "labels": {"reads": [{"table": "safety_incidents", "columns": ["openning_year", "invoicedate"]}], "writes": [{"table": "mart.mart_risk_score_hourly", "columns": ["openning_year", "invoicedate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table waterusage --target-dir /tmp/land\n", "labels": {"reads": [{"table": "waterusage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.coupon_use_delta SELECT a.productiondate, b.founding_location FROM mobile_plans a JOIN ods.clicks_delta b ON a.score = b.score\"\n", "labels": {"reads": [{"table": "mobile_plans", "columns": null}, {"table": "ods.clicks_delta", "columns": null}], "writes": [{"table": "stg.coupon_use_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO whale_sharks SELECT a.character, b.export_country FROM rural_projects a JOIN movies b ON a.supply_volume = b.supply_volume\"\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}, {"table": "movies", "columns": null}], "writes": [{"table": "whale_sharks", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT employee_id, num_solo_exhibitions FROM soccer_teams\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"stg.coupon_use_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "soccer_teams", "columns": ["employee_id", "num_solo_exhibitions"]}], "writes": [{"table": "stg.coupon_use_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO electric_vehicle_stats SELECT * FROM legacy\ncur.execute(\"SELECT heritage_site, fault_log_entry_id FROM art_exhibit_attendance LIMIT 495\")\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["heritage_site", "fault_log_entry_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM trends_2022\"\n", "labels": {"reads": [{"table": "trends_2022", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO defenseprojects (organizationname, incorporated_in) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "defenseprojects", "columns": ["organizationname", "incorporated_in"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT project_details, date_claim_settled FROM flights LIMIT 271;\nINSERT INTO completed_training SELECT injury_count, opening_hours FROM mars_spacecraft WHERE injury_count > 60;\nSQL\n", "labels": {"reads": [{"table": "flights", "columns": ["project_details", "date_claim_settled"]}, {"table": "mars_spacecraft", "columns": ["injury_count", "opening_hours"]}], "writes": [{"table": "completed_training", "columns": ["injury_count", "opening_hours"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO auctions SELECT a.strainid, b.sid FROM labor_costs a JOIN gamegenres b ON a.community_size = b.community_size\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "labor_costs", "columns": null}, {"table": "gamegenres", "columns": null}], "writes": [{"table": "auctions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fare_segments\"\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO labor_unions SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT roomname, person_id FROM submarine_canyons\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"vessels_2\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": ["roomname", "person_id"]}], "writes": [{"table": "vessels_2", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamedesigndata\").toPandas()\ndf[[\"num_of_staff\", \"frequency\"]].to_sql(\"impact_investments\", engine, index=False)\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": [{"table": "impact_investments", "columns": ["num_of_staff", "frequency"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"resource_extraction\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO device (injury_date, location_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "device", "columns": ["injury_date", "location_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table associatedheritages --columns treatment_type,document_type_description --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "associatedheritages", "columns": ["treatment_type", "document_type_description"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT number_city_affected, flag FROM displaced_people\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"taj_mahal_visitors\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "displaced_people", "columns": ["number_city_affected", "flag"]}], "writes": [{"table": "taj_mahal_visitors", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model communitycourtcases depends on wholesale_orders\ndbt run --select communitycourtcases --vars '{\"src\":\"wholesale_orders\"}'\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": null}], "writes": [{"table": "communitycourtcases", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO org_comms SELECT raceid, routeid, warehouse_name, number_of_platforms FROM features WHERE raceid > 453\"\n", "labels": {"reads": [{"table": "features", "columns": ["raceid", "routeid", "warehouse_name", "number_of_platforms"]}], "writes": [{"table": "org_comms", "columns": ["raceid", "routeid", "warehouse_name", "number_of_platforms"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dorm\", conn)\ndf.to_sql(\"acidification_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stay\").toPandas()\ndf[[\"is_hybrid\", \"serviceid\"]].to_sql(\"disaster_mitigation\", engine, index=False)\n", "labels": {"reads": [{"table": "stay", "columns": null}], "writes": [{"table": "disaster_mitigation", "columns": ["is_hybrid", "serviceid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"player_coach\")\nsrc.write.insertInto(\"claims_documents\", overwrite=True)\n", "labels": {"reads": [{"table": "player_coach", "columns": null}], "writes": [{"table": "claims_documents", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO product_categories SELECT hospitalid, participated_in_open_pedagogy, stream_id, vaccine_type FROM architect WHERE hospitalid > 432\"], check=True)\n", "labels": {"reads": [{"table": "architect", "columns": ["hospitalid", "participated_in_open_pedagogy", "stream_id", "vaccine_type"]}], "writes": [{"table": "product_categories", "columns": ["hospitalid", "participated_in_open_pedagogy", "stream_id", "vaccine_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO communitycourts SELECT a.chair_name, b.hub_id FROM humanitarian_assistance a JOIN total_consumption b ON a.gname = b.gname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "humanitarian_assistance", "columns": null}, {"table": "total_consumption", "columns": null}], "writes": [{"table": "communitycourts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.shipments_delta SELECT * FROM legacy\ncur.execute(\"SELECT ai_adoption, functional_area_description FROM route LIMIT 2\")\n", "labels": {"reads": [{"table": "route", "columns": ["ai_adoption", "functional_area_description"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO pharmasales SELECT coal_reserve_remaining, schedule_date, sample_date FROM mental_health_professionals_2 WHERE coal_reserve_remaining > 137\"\n", "labels": {"reads": [{"table": "mental_health_professionals_2", "columns": ["coal_reserve_remaining", "schedule_date", "sample_date"]}], "writes": [{"table": "pharmasales", "columns": ["coal_reserve_remaining", "schedule_date", "sample_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT crispr_id, attraction_type_description FROM inspections LIMIT 476\")\nresult = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO agriculturalinvestments SELECT menu_name, genderid FROM ads WHERE menu_name > 402\")\n", "labels": {"reads": [{"table": "inspections", "columns": ["crispr_id", "attraction_type_description"]}, {"table": "ads", "columns": ["menu_name", "genderid"]}], "writes": [{"table": "agriculturalinvestments", "columns": ["menu_name", "genderid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"mines\")\npush_to_sink(df, \"intelligence_personnel\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mines", "columns": null}], "writes": [{"table": "intelligence_personnel", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"makeup_products\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"smartcitytech\")\n", "labels": {"reads": [{"table": "makeup_products", "columns": null}], "writes": [{"table": "smartcitytech", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT aircraft_id, ticket_subject FROM customer_master_index LIMIT 218\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "customer_master_index", "columns": ["aircraft_id", "ticket_subject"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO apartments (service_name, catalog_level_number) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "apartments", "columns": ["service_name", "catalog_level_number"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT profession_count, grantid FROM vehicle_data\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"busmaintenance\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "vehicle_data", "columns": ["profession_count", "grantid"]}], "writes": [{"table": "busmaintenance", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO drilling_rigs SELECT * FROM legacy\ncur.execute(\"SELECT creationyear, experiment_name FROM buildings LIMIT 243\")\n", "labels": {"reads": [{"table": "buildings", "columns": ["creationyear", "experiment_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table bi.bi_events_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"educationprograms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"economic_diversification_efforts\")\n", "labels": {"reads": [{"table": "educationprograms", "columns": null}], "writes": [{"table": "economic_diversification_efforts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"public_schools\").toPandas()\ndf[[\"family_name\", \"customer_email_address\"]].to_sql(\"totalenergyproduction\", engine, index=False)\n", "labels": {"reads": [{"table": "public_schools", "columns": null}], "writes": [{"table": "totalenergyproduction", "columns": ["family_name", "customer_email_address"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM recycling_rates\", conn)\ndf.to_sql(\"emergency_calls\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "recycling_rates", "columns": null}], "writes": [{"table": "emergency_calls", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT payment_method_code, events FROM donations LIMIT 262;\nINSERT INTO galleryc SELECT personnelid, property_id, employee_address_id, shippingmethod FROM discount_coupons WHERE personnelid > 163;\nSQL\n", "labels": {"reads": [{"table": "donations", "columns": ["payment_method_code", "events"]}, {"table": "discount_coupons", "columns": ["personnelid", "property_id", "employee_address_id", "shippingmethod"]}], "writes": [{"table": "galleryc", "columns": ["personnelid", "property_id", "employee_address_id", "shippingmethod"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT matchid, enrollment FROM constructorstandings LIMIT 329\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "constructorstandings", "columns": ["matchid", "enrollment"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"patients\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.inventory_df\")\n", "labels": {"reads": [{"table": "patients", "columns": null}], "writes": [{"table": "dwd.inventory_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table defense_contracts --columns continent_id,transactions --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "defense_contracts", "columns": ["continent_id", "transactions"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM securityincidents\"\n", "labels": {"reads": [{"table": "securityincidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO review SELECT offer_id, asessment_outcome_code, approach, snatch FROM beverages WHERE offer_id > 132\"\n", "labels": {"reads": [{"table": "beverages", "columns": ["offer_id", "asessment_outcome_code", "approach", "snatch"]}], "writes": [{"table": "review", "columns": ["offer_id", "asessment_outcome_code", "approach", "snatch"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO whale_sightings SELECT loadingstart, club_id, enable_third_party_ads FROM documents WHERE loadingstart > 97\"\n", "labels": {"reads": [{"table": "documents", "columns": ["loadingstart", "club_id", "enable_third_party_ads"]}], "writes": [{"table": "whale_sightings", "columns": ["loadingstart", "club_id", "enable_third_party_ads"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.waste_type > 9).all()\n# src table: refugee_support\nengine.execute(\"INSERT INTO dispensaries SELECT * FROM refugee_support\")\n", "labels": {"reads": [{"table": "refugee_support", "columns": null}], "writes": [{"table": "dispensaries", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM africa_schema.african_mines\"\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO research_grants SELECT neighborhoodname, launch_date, product_description, company_name FROM disabilityadvocacy WHERE neighborhoodname > 253\"\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": ["neighborhoodname", "launch_date", "product_description", "company_name"]}], "writes": [{"table": "research_grants", "columns": ["neighborhoodname", "launch_date", "product_description", "company_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO city_tech SELECT a.strategy, b.detention_type_code FROM spacex_missions a JOIN disaster_response b ON a.market_id = b.market_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "spacex_missions", "columns": null}, {"table": "disaster_response", "columns": null}], "writes": [{"table": "city_tech", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT soil_moisture, num_cases FROM unions\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\ndf.to_sql(\"smart_grids\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "unions", "columns": ["soil_moisture", "num_cases"]}], "writes": [{"table": "smart_grids", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO police_stations SELECT a.middle_name, b.exit_strategy FROM product_review a JOIN biomes b ON a.has_parabens = b.has_parabens\"\n", "labels": {"reads": [{"table": "product_review", "columns": null}, {"table": "biomes", "columns": null}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO streams SELECT sellingprice, purchaseid FROM historicalcontexts WHERE sellingprice > 342\"\n", "labels": {"reads": [{"table": "historicalcontexts", "columns": ["sellingprice", "purchaseid"]}], "writes": [{"table": "streams", "columns": ["sellingprice", "purchaseid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO workshop SELECT training_name, disaster_type, last_maintenance FROM ads_payments_hourly WHERE training_name > 460;\nEOF\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": ["training_name", "disaster_type", "last_maintenance"]}], "writes": [{"table": "workshop", "columns": ["training_name", "disaster_type", "last_maintenance"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_products_hourly SELECT discount, chemical_name FROM support_programs WHERE discount > 387\"\n", "labels": {"reads": [{"table": "support_programs", "columns": ["discount", "chemical_name"]}], "writes": [{"table": "ads.ads_products_hourly", "columns": ["discount", "chemical_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.donationamount > 136).all()\n# src table: artifactanalysis\nengine.execute(\"INSERT INTO spacecraftmanufacturing SELECT * FROM artifactanalysis\")\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}], "writes": [{"table": "spacecraftmanufacturing", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT events, votes FROM investments LIMIT 260\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [{"table": "investments", "columns": ["events", "votes"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM field_rainfall\"\n", "labels": {"reads": [{"table": "field_rainfall", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO climate_projects SELECT attendance_id, ll_id, approach, prof_office FROM fabric WHERE attendance_id > 176\"], check=True)\n", "labels": {"reads": [{"table": "fabric", "columns": ["attendance_id", "ll_id", "approach", "prof_office"]}], "writes": [{"table": "climate_projects", "columns": ["attendance_id", "ll_id", "approach", "prof_office"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"financial_capability\").toPandas()\ndf[[\"browser_id\", \"manager_id\"]].to_sql(\"region\", engine, index=False)\n", "labels": {"reads": [{"table": "financial_capability", "columns": null}], "writes": [{"table": "region", "columns": ["browser_id", "manager_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_events_delta\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"inmates\")\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": [{"table": "inmates", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO fans_merchandise_basketball SELECT task, grant_name FROM vehicle_maintenance WHERE task > 277\"\n", "labels": {"reads": [{"table": "vehicle_maintenance", "columns": ["task", "grant_name"]}], "writes": [{"table": "fans_merchandise_basketball", "columns": ["task", "grant_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO investment_accounts SELECT bandmateid, browser_id, experiment_name FROM shared_scooters WHERE bandmateid > 89\"\n", "labels": {"reads": [{"table": "shared_scooters", "columns": ["bandmateid", "browser_id", "experiment_name"]}], "writes": [{"table": "investment_accounts", "columns": ["bandmateid", "browser_id", "experiment_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"makeup_products\")\nsrc.write.insertInto(\"elements_price\", overwrite=True)\n", "labels": {"reads": [{"table": "makeup_products", "columns": null}], "writes": [{"table": "elements_price", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bias_categories\")\nsrc.write.insertInto(\"biosensors.readings\", overwrite=True)\n", "labels": {"reads": [{"table": "bias_categories", "columns": null}], "writes": [{"table": "biosensors.readings", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO haircare_sales SELECT * FROM legacy\ncur.execute(\"SELECT phone_id, ai_algorithm_id FROM bank_info LIMIT 184\")\n", "labels": {"reads": [{"table": "bank_info", "columns": ["phone_id", "ai_algorithm_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table safety_incidents_india --columns acc_percent,amenity_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "safety_incidents_india", "columns": ["acc_percent", "amenity_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO al_jazeera_data SELECT sessiondate, condition_id, event_count FROM astronaut_missions WHERE sessiondate > 457\"\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": ["sessiondate", "condition_id", "event_count"]}], "writes": [{"table": "al_jazeera_data", "columns": ["sessiondate", "condition_id", "event_count"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table device --target-dir /tmp/land\n", "labels": {"reads": [{"table": "device", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM investments\", conn)\ndf.to_sql(\"france_culture\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "investments", "columns": null}], "writes": [{"table": "france_culture", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.sessions_full --columns num_transactions,booking_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.sessions_full", "columns": ["num_transactions", "booking_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO iot_sensors SELECT subscription_start_date, last_name FROM gamedesign WHERE subscription_start_date > 12\"], check=True)\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["subscription_start_date", "last_name"]}], "writes": [{"table": "iot_sensors", "columns": ["subscription_start_date", "last_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.dept_store_id > 13).all()\n# src table: waterconservationbudget\nengine.execute(\"INSERT INTO initiatives_3 SELECT * FROM waterconservationbudget\")\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": null}], "writes": [{"table": "initiatives_3", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table disease_prevalence --target-dir /tmp/land\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM socialimpactinvestments\"\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO landfill_capacity SELECT a.alid, b.ratingdate FROM equipment a JOIN conditions b ON a.menu_id = b.menu_id\"\n", "labels": {"reads": [{"table": "equipment", "columns": null}, {"table": "conditions", "columns": null}], "writes": [{"table": "landfill_capacity", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"inclusivehousing.affordablehousing\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "inclusivehousing.affordablehousing", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fairtradecertifications\", conn)\ndf.to_sql(\"drought_impact\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fairtradecertifications", "columns": null}], "writes": [{"table": "drought_impact", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM autonomousvehicleaccidents\", conn)\ndf.to_sql(\"injury_accident\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "autonomousvehicleaccidents", "columns": null}], "writes": [{"table": "injury_accident", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cityname, count_date FROM model_data LIMIT 441\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "model_data", "columns": ["cityname", "count_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"all_programs\")\nsink_to_store(df, \"agricultural_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "all_programs", "columns": null}], "writes": [{"table": "agricultural_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO trainmaintenance (fan_id, s_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "trainmaintenance", "columns": ["fan_id", "s_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_engagement\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ref_hotel_star_ratings\")\n", "labels": {"reads": [{"table": "community_engagement", "columns": null}], "writes": [{"table": "ref_hotel_star_ratings", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO news_stories SELECT date_opened, observation_id, agegroup FROM pharmasales WHERE date_opened > 94;\nEOF\n", "labels": {"reads": [{"table": "pharmasales", "columns": ["date_opened", "observation_id", "agegroup"]}], "writes": [{"table": "news_stories", "columns": ["date_opened", "observation_id", "agegroup"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cities SELECT rating_in_percent, follows_ethical_practices, strategy_name FROM e_scooter_trips WHERE rating_in_percent > 76\"], check=True)\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": ["rating_in_percent", "follows_ethical_practices", "strategy_name"]}], "writes": [{"table": "cities", "columns": ["rating_in_percent", "follows_ethical_practices", "strategy_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT fund_id, quantity_sold FROM police_emergencies LIMIT 166;\nINSERT INTO concerts SELECT elevation, pollutant_type, updated_at FROM ods.ods_member_point_df WHERE elevation > 33;\nSQL\n", "labels": {"reads": [{"table": "police_emergencies", "columns": ["fund_id", "quantity_sold"]}, {"table": "ods.ods_member_point_df", "columns": ["elevation", "pollutant_type", "updated_at"]}], "writes": [{"table": "concerts", "columns": ["elevation", "pollutant_type", "updated_at"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM distributors\"\n", "labels": {"reads": [{"table": "distributors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fashion_trend_data SELECT ai_id, school_code, dapp_name, movie FROM ads_sessions_di WHERE ai_id > 266\"\n", "labels": {"reads": [{"table": "ads_sessions_di", "columns": ["ai_id", "school_code", "dapp_name", "movie"]}], "writes": [{"table": "fashion_trend_data", "columns": ["ai_id", "school_code", "dapp_name", "movie"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT heritage_site, applicant FROM indian_ocean_wells LIMIT 355\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "indian_ocean_wells", "columns": ["heritage_site", "applicant"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO reporters SELECT start_date, well_name, amount_outstanding FROM player_attributes WHERE start_date > 334\"\n", "labels": {"reads": [{"table": "player_attributes", "columns": ["start_date", "well_name", "amount_outstanding"]}], "writes": [{"table": "reporters", "columns": ["start_date", "well_name", "amount_outstanding"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model price_data depends on ods.ods_device_log_delta\ndbt build -s price_data --vars '{\"src\":\"ods.ods_device_log_delta\"}'\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": null}], "writes": [{"table": "price_data", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model midwest_materials depends on ocean_salinity\ndbt run --models midwest_materials --vars '{\"source_table\":\"ocean_salinity\"}'\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}], "writes": [{"table": "midwest_materials", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT journal, fertilizer_id FROM noise_pollution LIMIT 156\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ads.ads_payments_delta SELECT showid, gas_production_2020, attraction_name, founder_gender FROM bi.bi_risk_score_full WHERE showid > 42\")\n", "labels": {"reads": [{"table": "noise_pollution", "columns": ["journal", "fertilizer_id"]}, {"table": "bi.bi_risk_score_full", "columns": ["showid", "gas_production_2020", "attraction_name", "founder_gender"]}], "writes": [{"table": "ads.ads_payments_delta", "columns": ["showid", "gas_production_2020", "attraction_name", "founder_gender"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.dws_clicks_full SELECT max_age, era FROM visual_arts WHERE max_age > 42\"], check=True)\n", "labels": {"reads": [{"table": "visual_arts", "columns": ["max_age", "era"]}], "writes": [{"table": "dws.dws_clicks_full", "columns": ["max_age", "era"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT therapy_type, habitat_name FROM carbon_offsets LIMIT 161;\nINSERT INTO stg.stg_exposure_daily SELECT union_id, trend, totalamount FROM creativeais WHERE union_id > 184;\nSQL\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["therapy_type", "habitat_name"]}, {"table": "creativeais", "columns": ["union_id", "trend", "totalamount"]}], "writes": [{"table": "stg.stg_exposure_daily", "columns": ["union_id", "trend", "totalamount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO satellite_deployment SELECT a.store_name, b.part_id FROM stg.device_log_df a JOIN stg.stg_risk_score_df b ON a.campaign = b.campaign\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": null}, {"table": "stg.stg_risk_score_df", "columns": null}], "writes": [{"table": "satellite_deployment", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_waste_generation\").toPandas()\ndf[[\"date_became_customer\", \"employee_name\"]].to_sql(\"concerts\", engine, index=False)\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": null}], "writes": [{"table": "concerts", "columns": ["date_became_customer", "employee_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM us_military_personnel\"\n", "labels": {"reads": [{"table": "us_military_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model hall_of_fame depends on colorado_river_basin\ndbt build -s hall_of_fame --vars '{\"source_table\":\"colorado_river_basin\"}'\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": null}], "writes": [{"table": "hall_of_fame", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nsql = \"INSERT INTO mart.mart_users SELECT a.program_expenses, b.routename FROM tourism a JOIN conditions b ON a.join_year = b.join_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tourism", "columns": null}, {"table": "conditions", "columns": null}], "writes": [{"table": "mart.mart_users", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT stu_dob, claimtype FROM humanitarianmissions LIMIT 404;\nINSERT INTO pollution_initiatives SELECT openingid, sale_id, dapp_name FROM artpieces WHERE openingid > 499;\nSQL\n", "labels": {"reads": [{"table": "humanitarianmissions", "columns": ["stu_dob", "claimtype"]}, {"table": "artpieces", "columns": ["openingid", "sale_id", "dapp_name"]}], "writes": [{"table": "pollution_initiatives", "columns": ["openingid", "sale_id", "dapp_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO eventattendance SELECT a.artpiecename, b.governor FROM purchases a JOIN shelters b ON a.resident_id = b.resident_id\"\n", "labels": {"reads": [{"table": "purchases", "columns": null}, {"table": "shelters", "columns": null}], "writes": [{"table": "eventattendance", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO smart_cities (route_short_name, governor) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "smart_cities", "columns": ["route_short_name", "governor"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_device_log_di SELECT a.issues, b.number_cities FROM county_public_safety a JOIN vessel_safety b ON a.hosts = b.hosts\"\n", "labels": {"reads": [{"table": "county_public_safety", "columns": null}, {"table": "vessel_safety", "columns": null}], "writes": [{"table": "ads.ads_device_log_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"music\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "music", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ratings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"workforce_development_programs\")\n", "labels": {"reads": [{"table": "ratings", "columns": null}], "writes": [{"table": "workforce_development_programs", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO food_safety_inspections (num_of_audience, industry_4_0) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "food_safety_inspections", "columns": ["num_of_audience", "industry_4_0"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO community_members SELECT review_id, zone, practice FROM opioid_overdoses WHERE review_id > 479\")\n", "labels": {"reads": [{"table": "opioid_overdoses", "columns": ["review_id", "zone", "practice"]}], "writes": [{"table": "community_members", "columns": ["review_id", "zone", "practice"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO project SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM op_projects\", conn)\ndf.to_sql(\"projecttimeline\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "op_projects", "columns": null}], "writes": [{"table": "projecttimeline", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO na_schema.hospitals SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mentalhealthprovider depends on bridgeconstruction\ndbt run -s mentalhealthprovider --vars '{\"src\":\"bridgeconstruction\"}'\n", "labels": {"reads": [{"table": "bridgeconstruction", "columns": null}], "writes": [{"table": "mentalhealthprovider", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO train (first_donation_date, gradepoint) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "train", "columns": ["first_donation_date", "gradepoint"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO lead_mines SELECT passenger_id, attendeeid FROM mart_vendors_full WHERE passenger_id > 181\"\n", "labels": {"reads": [{"table": "mart_vendors_full", "columns": ["passenger_id", "attendeeid"]}], "writes": [{"table": "lead_mines", "columns": ["passenger_id", "attendeeid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"precipitation_data\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stories\")\n", "labels": {"reads": [{"table": "precipitation_data", "columns": null}], "writes": [{"table": "stories", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO platformi SELECT invested, animal_type FROM makeup_products WHERE invested > 500\"\n", "labels": {"reads": [{"table": "makeup_products", "columns": ["invested", "animal_type"]}], "writes": [{"table": "platformi", "columns": ["invested", "animal_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO artifact_analysis SELECT dish_name, vehicle_flight_number, professional_development FROM mart_refunds WHERE dish_name > 81\"\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["dish_name", "vehicle_flight_number", "professional_development"]}], "writes": [{"table": "artifact_analysis", "columns": ["dish_name", "vehicle_flight_number", "professional_development"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hoursperweek, sector FROM uel_top10 LIMIT 433;\nINSERT INTO culturalcompetencytraining SELECT visit_details, game_name, email, transact_date FROM electricvehiclestats WHERE visit_details > 484;\nSQL\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["hoursperweek", "sector"]}, {"table": "electricvehiclestats", "columns": ["visit_details", "game_name", "email", "transact_date"]}], "writes": [{"table": "culturalcompetencytraining", "columns": ["visit_details", "game_name", "email", "transact_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.dw_campaigns_di\")\nsrc.write.insertInto(\"financial_capability_id\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.dw_campaigns_di", "columns": null}], "writes": [{"table": "financial_capability_id", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO laborstatistics SELECT dataset, ei_category FROM exam_results WHERE dataset > 312\"\n", "labels": {"reads": [{"table": "exam_results", "columns": ["dataset", "ei_category"]}], "writes": [{"table": "laborstatistics", "columns": ["dataset", "ei_category"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mining_operation_data\"\n", "labels": {"reads": [{"table": "mining_operation_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"militarypatents\").toPandas()\ndf[[\"owner_id\", \"representative_name\"]].to_sql(\"urban_initiatives\", engine, index=False)\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": [{"table": "urban_initiatives", "columns": ["owner_id", "representative_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO seasonalvegetables SELECT area_type, operation_count, emp_dob FROM docking WHERE area_type > 81;\nEOF\n", "labels": {"reads": [{"table": "docking", "columns": ["area_type", "operation_count", "emp_dob"]}], "writes": [{"table": "seasonalvegetables", "columns": ["area_type", "operation_count", "emp_dob"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT rating, trader_id FROM paris_train LIMIT 95;\nINSERT INTO bi.events_delta SELECT method_name, attribute_name, runtime FROM organization WHERE method_name > 169;\nSQL\n", "labels": {"reads": [{"table": "paris_train", "columns": ["rating", "trader_id"]}, {"table": "organization", "columns": ["method_name", "attribute_name", "runtime"]}], "writes": [{"table": "bi.events_delta", "columns": ["method_name", "attribute_name", "runtime"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.clicks SELECT * FROM legacy\ncur.execute(\"SELECT comment_count, financial_wellbeing_score FROM water_treatment_facilities LIMIT 369\")\n", "labels": {"reads": [{"table": "water_treatment_facilities", "columns": ["comment_count", "financial_wellbeing_score"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO co2price (catalog_name, green_building_certified) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "co2price", "columns": ["catalog_name", "green_building_certified"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model trafficviolations depends on professionals\ndbt build --models trafficviolations --vars 'source: professionals'\n", "labels": {"reads": [{"table": "professionals", "columns": null}], "writes": [{"table": "trafficviolations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO plots SELECT area, local_authority, district FROM conservation_projects WHERE area > 110\"\n", "labels": {"reads": [{"table": "conservation_projects", "columns": ["area", "local_authority", "district"]}], "writes": [{"table": "plots", "columns": ["area", "local_authority", "district"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT total_beds, round_number FROM bus_fare_collection LIMIT 15\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO workplaces SELECT line_1, local, year FROM college WHERE line_1 > 9\")\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": ["total_beds", "round_number"]}, {"table": "college", "columns": ["line_1", "local", "year"]}], "writes": [{"table": "workplaces", "columns": ["line_1", "local", "year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cyber_incidents SELECT asset_details, date_of_notes, instid FROM engineer_visits WHERE asset_details > 260\"\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["asset_details", "date_of_notes", "instid"]}], "writes": [{"table": "cyber_incidents", "columns": ["asset_details", "date_of_notes", "instid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO staff SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"broadband_providers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "broadband_providers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO zip_codes SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"worker_union\")\nsrc.write.insertInto(\"prepaid_mobile\", overwrite=True)\n", "labels": {"reads": [{"table": "worker_union", "columns": null}], "writes": [{"table": "prepaid_mobile", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table people --target-dir /tmp/land\n", "labels": {"reads": [{"table": "people", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO reporters SELECT location, material_id FROM chemical WHERE location > 389;\nEOF\n", "labels": {"reads": [{"table": "chemical", "columns": ["location", "material_id"]}], "writes": [{"table": "reporters", "columns": ["location", "material_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model experiments depends on visual_arts\ndbt run -s experiments --vars 'source: visual_arts'\n", "labels": {"reads": [{"table": "visual_arts", "columns": null}], "writes": [{"table": "experiments", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_ingredient\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"artists\")\n", "labels": {"reads": [{"table": "product_ingredient", "columns": null}], "writes": [{"table": "artists", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM total_capacity\"\n", "labels": {"reads": [{"table": "total_capacity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT exhibit_location, image_data FROM plots\", engine)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"military_spending\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "plots", "columns": ["exhibit_location", "image_data"]}], "writes": [{"table": "military_spending", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO food_safety_inspections SELECT project_category, organization_id FROM carbon_emissions WHERE project_category > 9\"], check=True)\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": ["project_category", "organization_id"]}], "writes": [{"table": "food_safety_inspections", "columns": ["project_category", "organization_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT therapy_sessions, professional_development FROM farms LIMIT 77\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "farms", "columns": ["therapy_sessions", "professional_development"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_member_point\").toPandas()\ndf[[\"community_id\", \"acc_bal\"]].to_sql(\"document_locations\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_member_point", "columns": null}], "writes": [{"table": "document_locations", "columns": ["community_id", "acc_bal"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO investment_rounds SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT capacity, departmentid FROM accessible_tech_categories LIMIT 353;\nINSERT INTO fish_farms SELECT host_id, impact_id, co2_emission, position FROM fields_production WHERE host_id > 162;\nSQL\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": ["capacity", "departmentid"]}, {"table": "fields_production", "columns": ["host_id", "impact_id", "co2_emission", "position"]}], "writes": [{"table": "fish_farms", "columns": ["host_id", "impact_id", "co2_emission", "position"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.recruitername > 170).all()\n# src table: ods_vendors_daily\nengine.execute(\"INSERT INTO ods.sessions SELECT * FROM ods_vendors_daily\")\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": null}], "writes": [{"table": "ods.sessions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT call_count, spacecraft_name FROM coal LIMIT 206\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "coal", "columns": ["call_count", "spacecraft_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO article_views SELECT * FROM legacy\ncur.execute(\"SELECT plant, festival_id FROM urban_initiatives LIMIT 403\")\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": ["plant", "festival_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table smartcitycosts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"textileworkers\")\nsrc.write.insertInto(\"drug_sales\", overwrite=True)\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": [{"table": "drug_sales", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model has_amenity depends on trip\ndbt build --models has_amenity --vars 'source: trip'\n", "labels": {"reads": [{"table": "trip", "columns": null}], "writes": [{"table": "has_amenity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO contract_transactions SELECT contract_start, report_type, theftdate, editor_id FROM mart.mart_products_hourly WHERE contract_start > 431\"\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": ["contract_start", "report_type", "theftdate", "editor_id"]}], "writes": [{"table": "contract_transactions", "columns": ["contract_start", "report_type", "theftdate", "editor_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"episodes\")\npersist_to_output(df, \"marine_life_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"movie_financials\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"art\")\n", "labels": {"reads": [{"table": "movie_financials", "columns": null}], "writes": [{"table": "art", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO scientists SELECT attribute_value, date_became_customer, attorney_last_name, college_location FROM dws_products WHERE attribute_value > 264;\nEOF\n", "labels": {"reads": [{"table": "dws_products", "columns": ["attribute_value", "date_became_customer", "attorney_last_name", "college_location"]}], "writes": [{"table": "scientists", "columns": ["attribute_value", "date_became_customer", "attorney_last_name", "college_location"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO product_info SELECT runtime, residence, production_value FROM tickets_3 WHERE runtime > 10\"\n", "labels": {"reads": [{"table": "tickets_3", "columns": ["runtime", "residence", "production_value"]}], "writes": [{"table": "product_info", "columns": ["runtime", "residence", "production_value"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dw_vendors_di SELECT factory_name, park_id, contract_address FROM landfillcapacitybycountry WHERE factory_name > 200\"], check=True)\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": ["factory_name", "park_id", "contract_address"]}], "writes": [{"table": "dw_vendors_di", "columns": ["factory_name", "park_id", "contract_address"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO patient_outcomes (mine_id, average) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "patient_outcomes", "columns": ["mine_id", "average"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM communitycourts\"\n", "labels": {"reads": [{"table": "communitycourts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO vessels_2 SELECT avg_yield, fair_labor, unitsperweek FROM channel WHERE avg_yield > 441\"\n", "labels": {"reads": [{"table": "channel", "columns": ["avg_yield", "fair_labor", "unitsperweek"]}], "writes": [{"table": "vessels_2", "columns": ["avg_yield", "fair_labor", "unitsperweek"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"atlantic_plate\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table whale_sharks --columns market,claim_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "whale_sharks", "columns": ["market", "claim_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO disease_prevalence SELECT mentalhealthscore, image_name FROM vessel_tracking WHERE mentalhealthscore > 50\")\n", "labels": {"reads": [{"table": "vessel_tracking", "columns": ["mentalhealthscore", "image_name"]}], "writes": [{"table": "disease_prevalence", "columns": ["mentalhealthscore", "image_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT pressure, invoice_details FROM uel_top10 LIMIT 251;\nINSERT INTO ads.ads_clicks_delta SELECT agegroup, pieces, total_passengers FROM dwd.dwd_exposure_df WHERE agegroup > 391;\nSQL\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["pressure", "invoice_details"]}, {"table": "dwd.dwd_exposure_df", "columns": ["agegroup", "pieces", "total_passengers"]}], "writes": [{"table": "ads.ads_clicks_delta", "columns": ["agegroup", "pieces", "total_passengers"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM workout\", conn)\ndf.to_sql(\"wastegeneration\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": [{"table": "wastegeneration", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO journal_committee SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"flight_safety\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO armed_forces SELECT address_line_1, hours, residence FROM view_product_availability WHERE address_line_1 > 180\"\n", "labels": {"reads": [{"table": "view_product_availability", "columns": ["address_line_1", "hours", "residence"]}], "writes": [{"table": "armed_forces", "columns": ["address_line_1", "hours", "residence"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO excavationsites SELECT program_type, strain_type, disaster_id FROM faculty_participates_in WHERE program_type > 215\"\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": ["program_type", "strain_type", "disaster_id"]}], "writes": [{"table": "excavationsites", "columns": ["program_type", "strain_type", "disaster_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_order_placed, menucategory FROM co_ownership_program LIMIT 346\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "co_ownership_program", "columns": ["date_order_placed", "menucategory"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT form_name, province_id FROM stellar_transactions LIMIT 49;\nINSERT INTO public.ev_sales SELECT donorname, other_hotel_details FROM vessel_registry WHERE donorname > 385;\nSQL\n", "labels": {"reads": [{"table": "stellar_transactions", "columns": ["form_name", "province_id"]}, {"table": "vessel_registry", "columns": ["donorname", "other_hotel_details"]}], "writes": [{"table": "public.ev_sales", "columns": ["donorname", "other_hotel_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contact_staff_id, dribbling FROM crime_incidents LIMIT 404\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO esports_teams SELECT mealid, report_id FROM manufacturers WHERE mealid > 111\")\n", "labels": {"reads": [{"table": "crime_incidents", "columns": ["contact_staff_id", "dribbling"]}, {"table": "manufacturers", "columns": ["mealid", "report_id"]}], "writes": [{"table": "esports_teams", "columns": ["mealid", "report_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model platformh depends on network_infrastructure\ndbt build --select platformh --vars '{\"src\":\"network_infrastructure\"}'\n", "labels": {"reads": [{"table": "network_infrastructure", "columns": null}], "writes": [{"table": "platformh", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO solana_transactions SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_test_taken, project_name FROM ingredients LIMIT 117\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "ingredients", "columns": ["date_test_taken", "project_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO jobs SELECT founder_identifies_as_lgbtq, violationid, mediatorid FROM cosmetics_sales WHERE founder_identifies_as_lgbtq > 222\"\n", "labels": {"reads": [{"table": "cosmetics_sales", "columns": ["founder_identifies_as_lgbtq", "violationid", "mediatorid"]}], "writes": [{"table": "jobs", "columns": ["founder_identifies_as_lgbtq", "violationid", "mediatorid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO danceevents SELECT * FROM legacy\ncur.execute(\"SELECT menuitemname, average_attendance FROM food_production LIMIT 446\")\n", "labels": {"reads": [{"table": "food_production", "columns": ["menuitemname", "average_attendance"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_investments\"\n", "labels": {"reads": [{"table": "climate_investments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO cosmetic_formula SELECT classtype, delivery_id, item_type, price_in_euros FROM infra_diversification WHERE classtype > 421\"\n", "labels": {"reads": [{"table": "infra_diversification", "columns": ["classtype", "delivery_id", "item_type", "price_in_euros"]}], "writes": [{"table": "cosmetic_formula", "columns": ["classtype", "delivery_id", "item_type", "price_in_euros"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_events_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"autonomousvehicleaccidents\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": [{"table": "autonomousvehicleaccidents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table threat_severity --columns treasurer_vote,patientid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "threat_severity", "columns": ["treasurer_vote", "patientid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"social_good_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "social_good_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"clothing_brands\").toPandas()\ndf[[\"price_in_dollar\", \"is_false\"]].to_sql(\"safety_research\", engine, index=False)\n", "labels": {"reads": [{"table": "clothing_brands", "columns": null}], "writes": [{"table": "safety_research", "columns": ["price_in_dollar", "is_false"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"legalaidrequests\")\nsrc.write.insertInto(\"london.stations\", overwrite=True)\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": null}], "writes": [{"table": "london.stations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"india_solar_power\").toPandas()\ndf[[\"order_quantity\", \"advocate_id\"]].to_sql(\"stg_orders_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "india_solar_power", "columns": null}], "writes": [{"table": "stg_orders_hourly", "columns": ["order_quantity", "advocate_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"coralreefs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"payments\")\n", "labels": {"reads": [{"table": "coralreefs", "columns": null}], "writes": [{"table": "payments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"landfill_capacity_city_v2\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"student_program_mapping\")\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": null}], "writes": [{"table": "student_program_mapping", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_development.transactions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"satellites_in_orbit\")\n", "labels": {"reads": [{"table": "community_development.transactions", "columns": null}], "writes": [{"table": "satellites_in_orbit", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM onlineengagement\"\n", "labels": {"reads": [{"table": "onlineengagement", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO military_aircraft_maintenance SELECT a.service_details, b.product_id FROM cosmetics.lipstick_spf_data a JOIN ods.campaigns_di b ON a.grant_amount = b.grant_amount\"\n", "labels": {"reads": [{"table": "cosmetics.lipstick_spf_data", "columns": null}, {"table": "ods.campaigns_di", "columns": null}], "writes": [{"table": "military_aircraft_maintenance", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO militarypersonnel SELECT projectid, do_value, publication_year, sustainability_score FROM publications WHERE projectid > 51\")\n", "labels": {"reads": [{"table": "publications", "columns": ["projectid", "do_value", "publication_year", "sustainability_score"]}], "writes": [{"table": "militarypersonnel", "columns": ["projectid", "do_value", "publication_year", "sustainability_score"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dws_campaigns_df depends on ads\ndbt run -s dws_campaigns_df --vars '{\"source_table\":\"ads\"}'\n", "labels": {"reads": [{"table": "ads", "columns": null}], "writes": [{"table": "dws_campaigns_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"zipcodes\")\nsrc.write.insertInto(\"experience\", overwrite=True)\n", "labels": {"reads": [{"table": "zipcodes", "columns": null}], "writes": [{"table": "experience", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"textileworkers\")\nsrc.write.insertInto(\"business_rates\", overwrite=True)\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": [{"table": "business_rates", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO customer_transactions SELECT complaint_id, branch, thing_id, patient_name FROM neodymium_prices WHERE complaint_id > 302\"\n", "labels": {"reads": [{"table": "neodymium_prices", "columns": ["complaint_id", "branch", "thing_id", "patient_name"]}], "writes": [{"table": "customer_transactions", "columns": ["complaint_id", "branch", "thing_id", "patient_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vrgames SELECT virtual_tour_sessions, bike_id FROM smart_grids WHERE virtual_tour_sessions > 57\"\n", "labels": {"reads": [{"table": "smart_grids", "columns": ["virtual_tour_sessions", "bike_id"]}], "writes": [{"table": "vrgames", "columns": ["virtual_tour_sessions", "bike_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO eia_schedule (organizationname, allocation_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "eia_schedule", "columns": ["organizationname", "allocation_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model sustainable_practices depends on international_visitors\ndbt build --models sustainable_practices --vars 'source: international_visitors'\n", "labels": {"reads": [{"table": "international_visitors", "columns": null}], "writes": [{"table": "sustainable_practices", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rooms, goldquantity FROM train LIMIT 242\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO al_jazeera_data SELECT effort_id, num_of_shops, countryname, shale_play FROM platformg WHERE effort_id > 189\")\n", "labels": {"reads": [{"table": "train", "columns": ["rooms", "goldquantity"]}, {"table": "platformg", "columns": ["effort_id", "num_of_shops", "countryname", "shale_play"]}], "writes": [{"table": "al_jazeera_data", "columns": ["effort_id", "num_of_shops", "countryname", "shale_play"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artist_concerts (date_account_opened, partitionid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artist_concerts", "columns": ["date_account_opened", "partitionid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.dwd_device_log_delta\", conn)\ndf.to_sql(\"fan_purchases\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": null}], "writes": [{"table": "fan_purchases", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table workers --columns visitid,development_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "workers", "columns": ["visitid", "development_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"red_line\").toPandas()\ndf[[\"operation_name\", \"policy_type\"]].to_sql(\"user\", engine, index=False)\n", "labels": {"reads": [{"table": "red_line", "columns": null}], "writes": [{"table": "user", "columns": ["operation_name", "policy_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT spacecraft_id, era FROM bi_refunds_daily LIMIT 410\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["spacecraft_id", "era"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM soccer_goals\"\n", "labels": {"reads": [{"table": "soccer_goals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO soil_moisture SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"episodes\")\nsave_to_sink(df, \"model_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": [{"table": "model_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO union_membership SELECT bank_name, denomination, ethnicity, fueldate FROM automation_tech WHERE bank_name > 248\"\n", "labels": {"reads": [{"table": "automation_tech", "columns": ["bank_name", "denomination", "ethnicity", "fueldate"]}], "writes": [{"table": "union_membership", "columns": ["bank_name", "denomination", "ethnicity", "fueldate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT visitors, site_name FROM energy_efficiency_projects LIMIT 33\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bus_routes SELECT mean_temperature_f, trip_city, catalog_level_name FROM ai_ethics WHERE mean_temperature_f > 195\")\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": ["visitors", "site_name"]}, {"table": "ai_ethics", "columns": ["mean_temperature_f", "trip_city", "catalog_level_name"]}], "writes": [{"table": "bus_routes", "columns": ["mean_temperature_f", "trip_city", "catalog_level_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table public_schools --target-dir /tmp/land\n", "labels": {"reads": [{"table": "public_schools", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"social_issues\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "social_issues", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO news_report SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg_payments_hourly SELECT min_depth, min_dew_point_f, minister FROM busmaintenance WHERE min_depth > 191;\nEOF\n", "labels": {"reads": [{"table": "busmaintenance", "columns": ["min_depth", "min_dew_point_f", "minister"]}], "writes": [{"table": "stg_payments_hourly", "columns": ["min_depth", "min_dew_point_f", "minister"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"customers_policies\")\nsrc.write.insertInto(\"lives_in\", overwrite=True)\n", "labels": {"reads": [{"table": "customers_policies", "columns": null}], "writes": [{"table": "lives_in", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO shark_biomass SELECT treatment_name, pid FROM user_likes WHERE treatment_name > 324\"\n", "labels": {"reads": [{"table": "user_likes", "columns": ["treatment_name", "pid"]}], "writes": [{"table": "shark_biomass", "columns": ["treatment_name", "pid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_coupon_use_df SELECT a.camera_lens_id, b.salesperson FROM streams a JOIN container_ships b ON a.content_type = b.content_type\"\n", "labels": {"reads": [{"table": "streams", "columns": null}, {"table": "container_ships", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dw.member_point_daily (primary_conference, dept_store_chain_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dw.member_point_daily", "columns": ["primary_conference", "dept_store_chain_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"casebilling\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"menu_vendors\")\n", "labels": {"reads": [{"table": "casebilling", "columns": null}], "writes": [{"table": "menu_vendors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT initiative_name, court_id FROM precision_farming_imagery LIMIT 354\")\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO producersnewmexico SELECT research_name, half, reign, coalid FROM recycling_rates_state WHERE research_name > 209\")\n", "labels": {"reads": [{"table": "precision_farming_imagery", "columns": ["initiative_name", "court_id"]}, {"table": "recycling_rates_state", "columns": ["research_name", "half", "reign", "coalid"]}], "writes": [{"table": "producersnewmexico", "columns": ["research_name", "half", "reign", "coalid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dws.cart_item_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.exposure SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT ip_address, school_colors FROM user_video_view LIMIT 14;\nINSERT INTO bi.bi_risk_score_delta SELECT amount_used, eventid, garment_type FROM shops WHERE amount_used > 150;\nSQL\n", "labels": {"reads": [{"table": "user_video_view", "columns": ["ip_address", "school_colors"]}, {"table": "shops", "columns": ["amount_used", "eventid", "garment_type"]}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["amount_used", "eventid", "garment_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO student_course_attendance SELECT * FROM legacy\ncur.execute(\"SELECT call_count, saleamount FROM bi.bi_events_df LIMIT 442\")\n", "labels": {"reads": [{"table": "bi.bi_events_df", "columns": ["call_count", "saleamount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO public.crime_types SELECT a.transaction_volume, b.workshop_name FROM classroom a JOIN nba_games b ON a.maxoccupancy = b.maxoccupancy\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "classroom", "columns": null}, {"table": "nba_games", "columns": null}], "writes": [{"table": "public.crime_types", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO route (products_this_year, report) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "route", "columns": ["products_this_year", "report"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recycling_rates_state SELECT * FROM legacy\ncur.execute(\"SELECT gtype, animal_type FROM voting_record LIMIT 231\")\n", "labels": {"reads": [{"table": "voting_record", "columns": ["gtype", "animal_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table news_report --target-dir /tmp/land\n", "labels": {"reads": [{"table": "news_report", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO exhibition_visits SELECT a.debate_id, b.cloud_cover FROM tourismproviders a JOIN midwest_materials b ON a.extraction_amount = b.extraction_amount\"\n", "labels": {"reads": [{"table": "tourismproviders", "columns": null}, {"table": "midwest_materials", "columns": null}], "writes": [{"table": "exhibition_visits", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM consumer_preference\", conn)\ndf.to_sql(\"ads_orders\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}], "writes": [{"table": "ads_orders", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO government.city SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT functional_area_code, vessel_name FROM recycling_stats LIMIT 361\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "recycling_stats", "columns": ["functional_area_code", "vessel_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.productcategory > 258).all()\n# src table: communityengagements\nengine.execute(\"INSERT INTO threats SELECT * FROM communityengagements\")\n", "labels": {"reads": [{"table": "communityengagements", "columns": null}], "writes": [{"table": "threats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nsql = \"INSERT INTO crypto_transactions SELECT a.subject_area_id, b.membership_card FROM party_host a JOIN fish_purchases b ON a.size_ha = b.size_ha\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "party_host", "columns": null}, {"table": "fish_purchases", "columns": null}], "writes": [{"table": "crypto_transactions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT number_deaths, is_compliant FROM inst LIMIT 461\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "inst", "columns": ["number_deaths", "is_compliant"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO projects SELECT * FROM legacy\ncur.execute(\"SELECT card_number, date_claim_settled FROM cmi_cross_references LIMIT 222\")\n", "labels": {"reads": [{"table": "cmi_cross_references", "columns": ["card_number", "date_claim_settled"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"school_bus\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"infrastructureprojects\")\n", "labels": {"reads": [{"table": "school_bus", "columns": null}], "writes": [{"table": "infrastructureprojects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.store_name > 64).all()\n# src table: daily_industrial_water_usage\nengine.execute(\"INSERT INTO ads.events SELECT * FROM daily_industrial_water_usage\")\n", "labels": {"reads": [{"table": "daily_industrial_water_usage", "columns": null}], "writes": [{"table": "ads.events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT is_accessible, vendorname FROM renewable_projects\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"dws_coupon_use_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["is_accessible", "vendorname"]}], "writes": [{"table": "dws_coupon_use_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO bi.refunds_daily SELECT item_type, province, units_sold FROM articles_es WHERE item_type > 226\"\n", "labels": {"reads": [{"table": "articles_es", "columns": ["item_type", "province", "units_sold"]}], "writes": [{"table": "bi.refunds_daily", "columns": ["item_type", "province", "units_sold"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mammals\", conn)\ndf.to_sql(\"aus_wellbeing\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mammals", "columns": null}], "writes": [{"table": "aus_wellbeing", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO engineer_skills SELECT customer_email, awardid FROM city_properties WHERE customer_email > 269\"\n", "labels": {"reads": [{"table": "city_properties", "columns": ["customer_email", "awardid"]}], "writes": [{"table": "engineer_skills", "columns": ["customer_email", "awardid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sanctuaryanimals SELECT * FROM legacy\ncur.execute(\"SELECT founding_year, nickname FROM infantmortalitydata LIMIT 455\")\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": ["founding_year", "nickname"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO online_travel_agency SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM marine_mammals\"\n", "labels": {"reads": [{"table": "marine_mammals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbonoffsetinitiatives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "carbonoffsetinitiatives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ecohousing depends on investments_esg\ndbt run -s ecohousing --vars 'source: investments_esg'\n", "labels": {"reads": [{"table": "investments_esg", "columns": null}], "writes": [{"table": "ecohousing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT account_number, safety_record FROM ods.ods_campaigns_delta LIMIT 91;\nINSERT INTO train_maintenance SELECT roomtype, contract_address, artworkid, player_id FROM ads.ads_clicks_delta WHERE roomtype > 471;\nSQL\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["account_number", "safety_record"]}, {"table": "ads.ads_clicks_delta", "columns": ["roomtype", "contract_address", "artworkid", "player_id"]}], "writes": [{"table": "train_maintenance", "columns": ["roomtype", "contract_address", "artworkid", "player_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO shops SELECT industry, waste_amount, brand_name FROM financial_capability_programs WHERE industry > 112\"\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": ["industry", "waste_amount", "brand_name"]}], "writes": [{"table": "shops", "columns": ["industry", "waste_amount", "brand_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO renewableprojects SELECT coownerid, indigenous FROM safety_data WHERE coownerid > 363\"], check=True)\n", "labels": {"reads": [{"table": "safety_data", "columns": ["coownerid", "indigenous"]}], "writes": [{"table": "renewableprojects", "columns": ["coownerid", "indigenous"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO safety_violations (preference_rating, ticket_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "safety_violations", "columns": ["preference_rating", "ticket_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"project_timelines\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "project_timelines", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table operate_company --target-dir /tmp/land\n", "labels": {"reads": [{"table": "operate_company", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table projecttimelinebybudget --target-dir /tmp/land\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO biotech.startups SELECT document_id, training_name, total_budget_percent_budgeted FROM cases WHERE document_id > 419\"\n", "labels": {"reads": [{"table": "cases", "columns": ["document_id", "training_name", "total_budget_percent_budgeted"]}], "writes": [{"table": "biotech.startups", "columns": ["document_id", "training_name", "total_budget_percent_budgeted"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT price, framework_id FROM totalenergyproduction LIMIT 114\")\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO conservation_initiatives SELECT workeridentity, opened_date FROM coral_reefs WHERE workeridentity > 216\")\n", "labels": {"reads": [{"table": "totalenergyproduction", "columns": ["price", "framework_id"]}, {"table": "coral_reefs", "columns": ["workeridentity", "opened_date"]}], "writes": [{"table": "conservation_initiatives", "columns": ["workeridentity", "opened_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO food_safety_inspections SELECT grantamount, habitat_name FROM fabrics WHERE grantamount > 253\"\n", "labels": {"reads": [{"table": "fabrics", "columns": ["grantamount", "habitat_name"]}], "writes": [{"table": "food_safety_inspections", "columns": ["grantamount", "habitat_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.num_workers > 112).all()\n# src table: apartment_buildings\nengine.execute(\"INSERT INTO restorative_justice_sentences SELECT * FROM apartment_buildings\")\n", "labels": {"reads": [{"table": "apartment_buildings", "columns": null}], "writes": [{"table": "restorative_justice_sentences", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT destination_name, offense FROM retailers LIMIT 432;\nINSERT INTO fabrics SELECT trainingtype, max_salary, transactionid FROM threat_intelligence_budget WHERE trainingtype > 390;\nSQL\n", "labels": {"reads": [{"table": "retailers", "columns": ["destination_name", "offense"]}, {"table": "threat_intelligence_budget", "columns": ["trainingtype", "max_salary", "transactionid"]}], "writes": [{"table": "fabrics", "columns": ["trainingtype", "max_salary", "transactionid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.scientist > 434).all()\n# src table: skincareproducts\nengine.execute(\"INSERT INTO digitalliteracytraining SELECT * FROM skincareproducts\")\n", "labels": {"reads": [{"table": "skincareproducts", "columns": null}], "writes": [{"table": "digitalliteracytraining", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"dw.shipments_df\")\npush_to_output(df, \"dw_risk_score_daily\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": null}], "writes": [{"table": "dw_risk_score_daily", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.stg_users_di SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO low_value_contracts SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fault_log\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"articles\")\n", "labels": {"reads": [{"table": "fault_log", "columns": null}], "writes": [{"table": "articles", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.clicks_df SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods_member_point_full SELECT workoutid, total_spent, dock_status FROM recall_reports WHERE workoutid > 229;\nEOF\n", "labels": {"reads": [{"table": "recall_reports", "columns": ["workoutid", "total_spent", "dock_status"]}], "writes": [{"table": "ods_member_point_full", "columns": ["workoutid", "total_spent", "dock_status"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT org_size, building_name FROM editor LIMIT 469\")\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO defense_projects SELECT sector_id, center, visit_month FROM military_contracts WHERE sector_id > 492\")\n", "labels": {"reads": [{"table": "editor", "columns": ["org_size", "building_name"]}, {"table": "military_contracts", "columns": ["sector_id", "center", "visit_month"]}], "writes": [{"table": "defense_projects", "columns": ["sector_id", "center", "visit_month"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ai_safety_incidents SELECT * FROM legacy\ncur.execute(\"SELECT serve_id, animal_id FROM lawyers LIMIT 234\")\n", "labels": {"reads": [{"table": "lawyers", "columns": ["serve_id", "animal_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO district_schools SELECT metric_id, crime_type FROM parking_fines WHERE metric_id > 467\"], check=True)\n", "labels": {"reads": [{"table": "parking_fines", "columns": ["metric_id", "crime_type"]}], "writes": [{"table": "district_schools", "columns": ["metric_id", "crime_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO recycling_centers SELECT missing_data, dance_form, issue_count, votes FROM date WHERE missing_data > 158\")\n", "labels": {"reads": [{"table": "date", "columns": ["missing_data", "dance_form", "issue_count", "votes"]}], "writes": [{"table": "recycling_centers", "columns": ["missing_data", "dance_form", "issue_count", "votes"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table recalls --columns area_name,quantity_sold --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "recalls", "columns": ["area_name", "quantity_sold"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO commercialbuildings SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO units SELECT * FROM legacy\ncur.execute(\"SELECT promotionid, retailer_name FROM communityengagement LIMIT 112\")\n", "labels": {"reads": [{"table": "communityengagement", "columns": ["promotionid", "retailer_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shops (warehousename, passenger_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shops", "columns": ["warehousename", "passenger_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_device_log_hourly SELECT * FROM legacy\ncur.execute(\"SELECT quality, clinic_type FROM experts LIMIT 28\")\n", "labels": {"reads": [{"table": "experts", "columns": ["quality", "clinic_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.cart_item_di (openning_year, building_phone) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.cart_item_di", "columns": ["openning_year", "building_phone"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO market_share SELECT wellid, enrollment_date FROM crops WHERE wellid > 110\"], check=True)\n", "labels": {"reads": [{"table": "crops", "columns": ["wellid", "enrollment_date"]}], "writes": [{"table": "market_share", "columns": ["wellid", "enrollment_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_fleet\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO feed (trip_distance, visitorid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "feed", "columns": ["trip_distance", "visitorid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT openingid, membership FROM container LIMIT 381;\nINSERT INTO casesbyyear SELECT male_id, price_in_dollar, case_type FROM accessibility_audits WHERE male_id > 354;\nSQL\n", "labels": {"reads": [{"table": "container", "columns": ["openingid", "membership"]}, {"table": "accessibility_audits", "columns": ["male_id", "price_in_dollar", "case_type"]}], "writes": [{"table": "casesbyyear", "columns": ["male_id", "price_in_dollar", "case_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO financialwellbeing SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM gold\"\n", "labels": {"reads": [{"table": "gold", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO wine SELECT job_title, product_description, animal, crane_id FROM mart_campaigns_delta WHERE job_title > 380;\nEOF\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": ["job_title", "product_description", "animal", "crane_id"]}], "writes": [{"table": "wine", "columns": ["job_title", "product_description", "animal", "crane_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.artwork_name > 438).all()\n# src table: bi.bi_risk_score_df\nengine.execute(\"INSERT INTO genre_songs SELECT * FROM bi.bi_risk_score_df\")\n", "labels": {"reads": [{"table": "bi.bi_risk_score_df", "columns": null}], "writes": [{"table": "genre_songs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads_users_hourly --columns subject_name,date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads_users_hourly", "columns": ["subject_name", "date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.issue > 109).all()\n# src table: wholesale_orders\nengine.execute(\"INSERT INTO ods.ods_campaigns_hourly SELECT * FROM wholesale_orders\")\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": null}], "writes": [{"table": "ods.ods_campaigns_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT resource_type, ties FROM tour_guides LIMIT 451\")\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO endowment SELECT date_incident_start, beds, payment_type_code FROM cargo_equipment WHERE date_incident_start > 111\")\n", "labels": {"reads": [{"table": "tour_guides", "columns": ["resource_type", "ties"]}, {"table": "cargo_equipment", "columns": ["date_incident_start", "beds", "payment_type_code"]}], "writes": [{"table": "endowment", "columns": ["date_incident_start", "beds", "payment_type_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"miningoperations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"communityevents\")\n", "labels": {"reads": [{"table": "miningoperations", "columns": null}], "writes": [{"table": "communityevents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT created_at, clean_jerk FROM producesupplier\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"invoice_lines\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "producesupplier", "columns": ["created_at", "clean_jerk"]}], "writes": [{"table": "invoice_lines", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT scientific_name, restypename FROM indie_artists LIMIT 36\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["scientific_name", "restypename"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"startup_founders\")\nsrc.write.insertInto(\"stg.stg_users_di\", overwrite=True)\n", "labels": {"reads": [{"table": "startup_founders", "columns": null}], "writes": [{"table": "stg.stg_users_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model restaurants depends on elements_price\ndbt build -s restaurants --vars '{\"source_table\":\"elements_price\"}'\n", "labels": {"reads": [{"table": "elements_price", "columns": null}], "writes": [{"table": "restaurants", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"rare_earth_companies\")\nsrc.write.insertInto(\"suppliersfairlabor\", overwrite=True)\n", "labels": {"reads": [{"table": "rare_earth_companies", "columns": null}], "writes": [{"table": "suppliersfairlabor", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hotel_chain_name > 386).all()\n# src table: contract_negotiations\nengine.execute(\"INSERT INTO space_exploration SELECT * FROM contract_negotiations\")\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": null}], "writes": [{"table": "space_exploration", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT lastname, snatch FROM country_landfill_capacity LIMIT 35\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "country_landfill_capacity", "columns": ["lastname", "snatch"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO public.forest_stats (area_type, online_dispute_resolution) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "public.forest_stats", "columns": ["area_type", "online_dispute_resolution"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO livestock SELECT a.typical_buying_price, b.vesselname FROM investor a JOIN playergamedata b ON a.unit_name = b.unit_name\"\n", "labels": {"reads": [{"table": "investor", "columns": null}, {"table": "playergamedata", "columns": null}], "writes": [{"table": "livestock", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO financialwellbeing SELECT member_id, consultations FROM gamedesign WHERE member_id > 392\")\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["member_id", "consultations"]}], "writes": [{"table": "financialwellbeing", "columns": ["member_id", "consultations"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT assistingnurse, incident_region FROM fish_biomass LIMIT 45\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO stg_users_daily SELECT shippedcost, reason, publication_year, fare_amount FROM expenditure WHERE shippedcost > 49\")\n", "labels": {"reads": [{"table": "fish_biomass", "columns": ["assistingnurse", "incident_region"]}, {"table": "expenditure", "columns": ["shippedcost", "reason", "publication_year", "fare_amount"]}], "writes": [{"table": "stg_users_daily", "columns": ["shippedcost", "reason", "publication_year", "fare_amount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT categoryname, specialty FROM rooms LIMIT 1\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "rooms", "columns": ["categoryname", "specialty"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fossil_fuel_vehicles_japan SELECT data_type, roomname, team_id_br FROM basketball_teams WHERE data_type > 419\"\n", "labels": {"reads": [{"table": "basketball_teams", "columns": ["data_type", "roomname", "team_id_br"]}], "writes": [{"table": "fossil_fuel_vehicles_japan", "columns": ["data_type", "roomname", "team_id_br"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO projectemployees SELECT receipt_date, date_joined_staff, session_name FROM drought_impact WHERE receipt_date > 270\"\n", "labels": {"reads": [{"table": "drought_impact", "columns": ["receipt_date", "date_joined_staff", "session_name"]}], "writes": [{"table": "projectemployees", "columns": ["receipt_date", "date_joined_staff", "session_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO safety_records SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"problem_log\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"carbon_offsets.carbon_offsets\")\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO habitat SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT wins, certification FROM renewableenergyprojects LIMIT 313;\nINSERT INTO dwd.events_daily SELECT soil_moisture, sales_channel, conferenceid, enable_third_party_ads FROM dates WHERE soil_moisture > 212;\nSQL\n", "labels": {"reads": [{"table": "renewableenergyprojects", "columns": ["wins", "certification"]}, {"table": "dates", "columns": ["soil_moisture", "sales_channel", "conferenceid", "enable_third_party_ads"]}], "writes": [{"table": "dwd.events_daily", "columns": ["soil_moisture", "sales_channel", "conferenceid", "enable_third_party_ads"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM world_heritage_sites\", conn)\ndf.to_sql(\"low_value_contracts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": null}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sourcing SELECT ranking, union_member_id, storename, start_station_id FROM gamedesign WHERE ranking > 119\"\n", "labels": {"reads": [{"table": "gamedesign", "columns": ["ranking", "union_member_id", "storename", "start_station_id"]}], "writes": [{"table": "sourcing", "columns": ["ranking", "union_member_id", "storename", "start_station_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mediatype\").toPandas()\ndf[[\"virtual_tour_sessions\", \"skill_id\"]].to_sql(\"government.region\", engine, index=False)\n", "labels": {"reads": [{"table": "mediatype", "columns": null}], "writes": [{"table": "government.region", "columns": ["virtual_tour_sessions", "skill_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vehiclemodels SELECT date_of_attendance, nutrient_level, date_joined_staff FROM stg.stg_exposure_daily WHERE date_of_attendance > 356\"], check=True)\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["date_of_attendance", "nutrient_level", "date_joined_staff"]}], "writes": [{"table": "vehiclemodels", "columns": ["date_of_attendance", "nutrient_level", "date_joined_staff"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO price_data SELECT date_valid_from, watertemp, process_id, sensor_type FROM class WHERE date_valid_from > 454\"\n", "labels": {"reads": [{"table": "class", "columns": ["date_valid_from", "watertemp", "process_id", "sensor_type"]}], "writes": [{"table": "price_data", "columns": ["date_valid_from", "watertemp", "process_id", "sensor_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO threat_intelligence_budget SELECT number_city_affected, bathroom_count, bandmate, contractor FROM chemical_composition WHERE number_city_affected > 149\"], check=True)\n", "labels": {"reads": [{"table": "chemical_composition", "columns": ["number_city_affected", "bathroom_count", "bandmate", "contractor"]}], "writes": [{"table": "threat_intelligence_budget", "columns": ["number_city_affected", "bathroom_count", "bandmate", "contractor"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.mart_device_log_delta (person_id, has_spf) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_device_log_delta", "columns": ["person_id", "has_spf"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"birds\")\nsrc.write.insertInto(\"renewable_power\", overwrite=True)\n", "labels": {"reads": [{"table": "birds", "columns": null}], "writes": [{"table": "renewable_power", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO dws_coupon_use_df SELECT a.shipping_agent_name, b.userid FROM mediatype a JOIN urban_agriculture_initiatives b ON a.update_date = b.update_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mediatype", "columns": null}, {"table": "urban_agriculture_initiatives", "columns": null}], "writes": [{"table": "dws_coupon_use_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"stg.risk_score_hourly\")\nexport_to_sink(df, \"spending\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.risk_score_hourly", "columns": null}], "writes": [{"table": "spending", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO academic_publications SELECT allergytype, network FROM contract_negotiations WHERE allergytype > 221\"], check=True)\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["allergytype", "network"]}], "writes": [{"table": "academic_publications", "columns": ["allergytype", "network"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"budget_allocations\")\nexport_to_output(df, \"casebilling\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "casebilling", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO entrepreneur SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO community_health_workers SELECT astronaut, vrgameid, therapy_sessions FROM cargo_handling WHERE astronaut > 290\")\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["astronaut", "vrgameid", "therapy_sessions"]}], "writes": [{"table": "community_health_workers", "columns": ["astronaut", "vrgameid", "therapy_sessions"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"tweets\")\npush_to_warehouse(df, \"cargos\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tweets", "columns": null}], "writes": [{"table": "cargos", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO creative_ai (courtid, director) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "creative_ai", "columns": ["courtid", "director"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM recruiters\", conn)\ndf.to_sql(\"mart.mart_payments_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "recruiters", "columns": null}], "writes": [{"table": "mart.mart_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO video_content SELECT feb, pollution_id FROM military_expenditure WHERE feb > 393\"\n", "labels": {"reads": [{"table": "military_expenditure", "columns": ["feb", "pollution_id"]}], "writes": [{"table": "video_content", "columns": ["feb", "pollution_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"biosensors.readings\")\nsrc.write.insertInto(\"satellite_missions_large\", overwrite=True)\n", "labels": {"reads": [{"table": "biosensors.readings", "columns": null}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_investments\").toPandas()\ndf[[\"num_investments\", \"license_number\"]].to_sql(\"rural_hospitals\", engine, index=False)\n", "labels": {"reads": [{"table": "climate_investments", "columns": null}], "writes": [{"table": "rural_hospitals", "columns": ["num_investments", "license_number"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO advisor SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO labour_productivity SELECT trainingtype, build_date FROM season_assists WHERE trainingtype > 200;\nEOF\n", "labels": {"reads": [{"table": "season_assists", "columns": ["trainingtype", "build_date"]}], "writes": [{"table": "labour_productivity", "columns": ["trainingtype", "build_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO catalogs (count, donor) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "catalogs", "columns": ["count", "donor"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_member_point_hourly SELECT a.app_id, b.produceid FROM waste_generation a JOIN hotel_chains b ON a.item_price = b.item_price\"\n", "labels": {"reads": [{"table": "waste_generation", "columns": null}, {"table": "hotel_chains", "columns": null}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO runs (donator_name, author) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "runs", "columns": ["donator_name", "author"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table accessible_tech_categories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT payment_method, college_id FROM dws_coupon_use LIMIT 128;\nINSERT INTO complaints_breakdown SELECT singer_id, char_cells, artist_id, flight_number FROM cities WHERE singer_id > 281;\nSQL\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["payment_method", "college_id"]}, {"table": "cities", "columns": ["singer_id", "char_cells", "artist_id", "flight_number"]}], "writes": [{"table": "complaints_breakdown", "columns": ["singer_id", "char_cells", "artist_id", "flight_number"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO indigenous_communities SELECT running_time, signup_date, offense FROM mailshot_campaigns WHERE running_time > 310\")\n", "labels": {"reads": [{"table": "mailshot_campaigns", "columns": ["running_time", "signup_date", "offense"]}], "writes": [{"table": "indigenous_communities", "columns": ["running_time", "signup_date", "offense"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.personal_name > 75).all()\n# src table: cuisine\nengine.execute(\"INSERT INTO gameplatforms SELECT * FROM cuisine\")\n", "labels": {"reads": [{"table": "cuisine", "columns": null}], "writes": [{"table": "gameplatforms", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO expensive_space_missions (volunteerhourid, eventattendance) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "expensive_space_missions", "columns": ["volunteerhourid", "eventattendance"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mental_health_parity_violations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"producers\")\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}], "writes": [{"table": "producers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table measurement --columns reported_by_staff_id,museumid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "measurement", "columns": ["reported_by_staff_id", "museumid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamegenres\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shops\")\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": [{"table": "shops", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO energy_consumption SELECT excavation_site, avg_usage, date_contact_to FROM satellites WHERE excavation_site > 443\"], check=True)\n", "labels": {"reads": [{"table": "satellites", "columns": ["excavation_site", "avg_usage", "date_contact_to"]}], "writes": [{"table": "energy_consumption", "columns": ["excavation_site", "avg_usage", "date_contact_to"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO wastewater_treatment_plants SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"station_emergencies\")\nsrc.write.insertInto(\"bi.inventory_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "station_emergencies", "columns": null}], "writes": [{"table": "bi.inventory_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical\").toPandas()\ndf[[\"camera_lens_id\", \"outcome_date\"]].to_sql(\"maintenance_engineers\", engine, index=False)\n", "labels": {"reads": [{"table": "chemical", "columns": null}], "writes": [{"table": "maintenance_engineers", "columns": ["camera_lens_id", "outcome_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ocean_shipping.cargo --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ocean_shipping.cargo", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO life_expectancy SELECT bedtype, race, race_ethnicity_id FROM training_programs WHERE bedtype > 312\"\n", "labels": {"reads": [{"table": "training_programs", "columns": ["bedtype", "race", "race_ethnicity_id"]}], "writes": [{"table": "life_expectancy", "columns": ["bedtype", "race", "race_ethnicity_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO flu_cases SELECT payment_id, rank FROM dwd.dwd_member_point_full WHERE payment_id > 291\"\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_full", "columns": ["payment_id", "rank"]}], "writes": [{"table": "flu_cases", "columns": ["payment_id", "rank"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO vehicle_maintenance SELECT height, bank_name FROM train_station WHERE height > 217;\nEOF\n", "labels": {"reads": [{"table": "train_station", "columns": ["height", "bank_name"]}], "writes": [{"table": "vehicle_maintenance", "columns": ["height", "bank_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO union_members SELECT * FROM legacy\ncur.execute(\"SELECT staystart, sensor_reading FROM aircraft LIMIT 72\")\n", "labels": {"reads": [{"table": "aircraft", "columns": ["staystart", "sensor_reading"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.assessment_date > 179).all()\n# src table: soil_moisture\nengine.execute(\"INSERT INTO water_usage SELECT * FROM soil_moisture\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}], "writes": [{"table": "water_usage", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT enable_location_tracking, retweets FROM global_tournament LIMIT 370;\nINSERT INTO papers SELECT attribute_id, data_usage FROM customer_payments WHERE attribute_id > 88;\nSQL\n", "labels": {"reads": [{"table": "global_tournament", "columns": ["enable_location_tracking", "retweets"]}, {"table": "customer_payments", "columns": ["attribute_id", "data_usage"]}], "writes": [{"table": "papers", "columns": ["attribute_id", "data_usage"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO college SELECT budget_allocated, exhibitionname, technique_id FROM investment WHERE budget_allocated > 135\"\n", "labels": {"reads": [{"table": "investment", "columns": ["budget_allocated", "exhibitionname", "technique_id"]}], "writes": [{"table": "college", "columns": ["budget_allocated", "exhibitionname", "technique_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT emp_fname, network FROM school_enrollment LIMIT 260\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO agricultural_innovation SELECT pets_allowed_yn, treatment FROM fish_stock WHERE pets_allowed_yn > 280\")\n", "labels": {"reads": [{"table": "school_enrollment", "columns": ["emp_fname", "network"]}, {"table": "fish_stock", "columns": ["pets_allowed_yn", "treatment"]}], "writes": [{"table": "agricultural_innovation", "columns": ["pets_allowed_yn", "treatment"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO militaryequipmentsales SELECT active_from_date, payment_method_code, resource_type, restorative_justice FROM dw.inventory_delta WHERE active_from_date > 445;\nEOF\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": ["active_from_date", "payment_method_code", "resource_type", "restorative_justice"]}], "writes": [{"table": "militaryequipmentsales", "columns": ["active_from_date", "payment_method_code", "resource_type", "restorative_justice"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dwd.exposure_hourly SELECT project_date, factory_name, postal_code FROM ads.ads_risk_score_hourly WHERE project_date > 71\")\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": ["project_date", "factory_name", "postal_code"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": ["project_date", "factory_name", "postal_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climatedata\"\n", "labels": {"reads": [{"table": "climatedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT lieutenant_governor, apid FROM doctors LIMIT 56;\nINSERT INTO carbonoffsetinitiatives SELECT mineid, height, business_name FROM leo_missions WHERE mineid > 188;\nSQL\n", "labels": {"reads": [{"table": "doctors", "columns": ["lieutenant_governor", "apid"]}, {"table": "leo_missions", "columns": ["mineid", "height", "business_name"]}], "writes": [{"table": "carbonoffsetinitiatives", "columns": ["mineid", "height", "business_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO team_members SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM surveylocations\"\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT share_count, approach FROM military_contracts\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"trainingprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "military_contracts", "columns": ["share_count", "approach"]}], "writes": [{"table": "trainingprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO events (detection_date, device) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "events", "columns": ["detection_date", "device"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ods_shipments_df SELECT market_value, recruiterid, contractorid FROM maintenance_schedule WHERE market_value > 353\"], check=True)\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": ["market_value", "recruiterid", "contractorid"]}], "writes": [{"table": "ods_shipments_df", "columns": ["market_value", "recruiterid", "contractorid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dws.dws_member_point_di SELECT vehicle_type, uid FROM show WHERE vehicle_type > 495;\nEOF\n", "labels": {"reads": [{"table": "show", "columns": ["vehicle_type", "uid"]}], "writes": [{"table": "dws.dws_member_point_di", "columns": ["vehicle_type", "uid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO airportdata SELECT * FROM legacy\ncur.execute(\"SELECT founder_race, response_time FROM taj_mahal_visitors LIMIT 379\")\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": ["founder_race", "response_time"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO algorithmic_fairness_incidents SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"courts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"asia_events\")\n", "labels": {"reads": [{"table": "courts", "columns": null}], "writes": [{"table": "asia_events", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bike_stations SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM criminal_justice_reform_initiatives\", conn)\ndf.to_sql(\"bi.bi_member_point\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": null}], "writes": [{"table": "bi.bi_member_point", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT train_id, bank_name FROM recalls LIMIT 203\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO satisfaction SELECT open_date, focal_length_mm, co2_emissions FROM performingartsprograms WHERE open_date > 359\")\n", "labels": {"reads": [{"table": "recalls", "columns": ["train_id", "bank_name"]}, {"table": "performingartsprograms", "columns": ["open_date", "focal_length_mm", "co2_emissions"]}], "writes": [{"table": "satisfaction", "columns": ["open_date", "focal_length_mm", "co2_emissions"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"african_tourism\")\nsrc.write.insertInto(\"nutrition_facts\", overwrite=True)\n", "labels": {"reads": [{"table": "african_tourism", "columns": null}], "writes": [{"table": "nutrition_facts", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ota_revenue\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donations_insert_2 SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO port_office SELECT a.safety_record, b.is_accessible FROM product_details a JOIN city_waste_generation b ON a.transaction_product = b.transaction_product\"\n", "labels": {"reads": [{"table": "product_details", "columns": null}, {"table": "city_waste_generation", "columns": null}], "writes": [{"table": "port_office", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.dwd_device_log_delta (life_expectancy, certification) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_device_log_delta", "columns": ["life_expectancy", "certification"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT routeid, farmer_name FROM airport LIMIT 420\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "airport", "columns": ["routeid", "farmer_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table volunteer_events --target-dir /tmp/land\n", "labels": {"reads": [{"table": "volunteer_events", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table skincareinventory --columns writer,incidents --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "skincareinventory", "columns": ["writer", "incidents"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO operations SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.founder > 138).all()\n# src table: supportservices\nengine.execute(\"INSERT INTO coowners SELECT * FROM supportservices\")\n", "labels": {"reads": [{"table": "supportservices", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ship SELECT city_traffic_speed, menu_name FROM artifact_analysis WHERE city_traffic_speed > 176\")\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": ["city_traffic_speed", "menu_name"]}], "writes": [{"table": "ship", "columns": ["city_traffic_speed", "menu_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table algorithmic_fairness --target-dir /tmp/land\n", "labels": {"reads": [{"table": "algorithmic_fairness", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"course_authors_and_tutors\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bridgeconstruction SELECT grant_id, allergy, min_depth FROM mart_vendors_full WHERE grant_id > 429\")\n", "labels": {"reads": [{"table": "mart_vendors_full", "columns": ["grant_id", "allergy", "min_depth"]}], "writes": [{"table": "bridgeconstruction", "columns": ["grant_id", "allergy", "min_depth"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"undergoes\").toPandas()\ndf[[\"destroyed_by_employee_id\", \"eventtype\"]].to_sql(\"fields_production\", engine, index=False)\n", "labels": {"reads": [{"table": "undergoes", "columns": null}], "writes": [{"table": "fields_production", "columns": ["destroyed_by_employee_id", "eventtype"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT launch_date, menu_id FROM carbon_prices_3 LIMIT 488\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["launch_date", "menu_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO routes SELECT ngo_name, categoryid, tourist_id FROM broadband_providers WHERE ngo_name > 29\"\n", "labels": {"reads": [{"table": "broadband_providers", "columns": ["ngo_name", "categoryid", "tourist_id"]}], "writes": [{"table": "routes", "columns": ["ngo_name", "categoryid", "tourist_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table armed_forces --target-dir /tmp/land\n", "labels": {"reads": [{"table": "armed_forces", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO wedding (leader, itemname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "wedding", "columns": ["leader", "itemname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table usdaviolations --columns fiscal_year,well_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "usdaviolations", "columns": ["fiscal_year", "well_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO catalog_contents_additional_attributes SELECT a.trench_id, b.advocate_name FROM rural_feeder_roads a JOIN gene b ON a.dock_count = b.dock_count\"\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": null}, {"table": "gene", "columns": null}], "writes": [{"table": "catalog_contents_additional_attributes", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO claims_documents SELECT a.balance, b.routename FROM hall_of_fame a JOIN stg.stg_coupon_use_di b ON a.name_first = b.name_first\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": null}, {"table": "stg.stg_coupon_use_di", "columns": null}], "writes": [{"table": "claims_documents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fault_short_name, treatment_year FROM stg.stg_events_di\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"recycledmaterialsgarments\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": ["fault_short_name", "treatment_year"]}], "writes": [{"table": "recycledmaterialsgarments", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table london.stations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "london.stations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO gymnast SELECT water_temp, training_name, platformid, crop_id FROM support_programs WHERE water_temp > 413;\nEOF\n", "labels": {"reads": [{"table": "support_programs", "columns": ["water_temp", "training_name", "platformid", "crop_id"]}], "writes": [{"table": "gymnast", "columns": ["water_temp", "training_name", "platformid", "crop_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT calories, clinic_id FROM nba\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"open_data_initiatives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "nba", "columns": ["calories", "clinic_id"]}], "writes": [{"table": "open_data_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT payment_method_code, review_id FROM employeedemographics\", engine)\nimport logging\ndf.to_sql(\"electronics_factories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "employeedemographics", "columns": ["payment_method_code", "review_id"]}], "writes": [{"table": "electronics_factories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_orders_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.scan_date > 67).all()\n# src table: claims_documents\nengine.execute(\"INSERT INTO functional_areas SELECT * FROM claims_documents\")\n", "labels": {"reads": [{"table": "claims_documents", "columns": null}], "writes": [{"table": "functional_areas", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO blockchain_tech SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO attorneylocationyear SELECT zone, amount, other_details, served_subscribers FROM league_x WHERE zone > 168\"\n", "labels": {"reads": [{"table": "league_x", "columns": ["zone", "amount", "other_details", "served_subscribers"]}], "writes": [{"table": "attorneylocationyear", "columns": ["zone", "amount", "other_details", "served_subscribers"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO renewable_energy_investments SELECT wifi, farmland_id, site_id, annual_interchanges FROM droughthistory WHERE wifi > 318\"\n", "labels": {"reads": [{"table": "droughthistory", "columns": ["wifi", "farmland_id", "site_id", "annual_interchanges"]}], "writes": [{"table": "renewable_energy_investments", "columns": ["wifi", "farmland_id", "site_id", "annual_interchanges"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO territory.human_rights_data SELECT * FROM legacy\ncur.execute(\"SELECT tech_type, resolved FROM militarydrones LIMIT 343\")\n", "labels": {"reads": [{"table": "militarydrones", "columns": ["tech_type", "resolved"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO state_usage SELECT org_name, treatment_year FROM phishing_targets WHERE org_name > 274;\nEOF\n", "labels": {"reads": [{"table": "phishing_targets", "columns": ["org_name", "treatment_year"]}], "writes": [{"table": "state_usage", "columns": ["org_name", "treatment_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO design_standards SELECT strat_name, area_sqkm FROM hospital WHERE strat_name > 90\")\n", "labels": {"reads": [{"table": "hospital", "columns": ["strat_name", "area_sqkm"]}], "writes": [{"table": "design_standards", "columns": ["strat_name", "area_sqkm"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_systems\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_member_point_full\")\n", "labels": {"reads": [{"table": "ai_systems", "columns": null}], "writes": [{"table": "dwd.dwd_member_point_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cultural_heritage SELECT valuation, route_id FROM obesity WHERE valuation > 10\"], check=True)\n", "labels": {"reads": [{"table": "obesity", "columns": ["valuation", "route_id"]}], "writes": [{"table": "cultural_heritage", "columns": ["valuation", "route_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_refunds_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO traditionalarts SELECT led_by, hire_date, temperature, sustainability_rating FROM useracct WHERE led_by > 214\"], check=True)\n", "labels": {"reads": [{"table": "useracct", "columns": ["led_by", "hire_date", "temperature", "sustainability_rating"]}], "writes": [{"table": "traditionalarts", "columns": ["led_by", "hire_date", "temperature", "sustainability_rating"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO mart_payments_df SELECT hospitalid, cultural_diversity FROM bi.refunds_daily WHERE hospitalid > 479\"\n", "labels": {"reads": [{"table": "bi.refunds_daily", "columns": ["hospitalid", "cultural_diversity"]}], "writes": [{"table": "mart_payments_df", "columns": ["hospitalid", "cultural_diversity"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM disaster_response\", conn)\ndf.to_sql(\"school_districts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "disaster_response", "columns": null}], "writes": [{"table": "school_districts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"incarcerated\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"electric_buses\")\n", "labels": {"reads": [{"table": "incarcerated", "columns": null}], "writes": [{"table": "electric_buses", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT balance, worker_id FROM dws.dws_events_hourly\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"animal_budget\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": ["balance", "worker_id"]}], "writes": [{"table": "animal_budget", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO southchinasea.wells SELECT a.principal_activities, b.role_description FROM wholesale_orders a JOIN public_works_projects b ON a.funding_source_type = b.funding_source_type\"\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": null}, {"table": "public_works_projects", "columns": null}], "writes": [{"table": "southchinasea.wells", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attribute_data_type, productid FROM architect LIMIT 26\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "architect", "columns": ["attribute_data_type", "productid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO bi.events_delta SELECT a.casestatus, b.openning_year FROM local_impact_japan a JOIN tracks b ON a.artist_nationality = b.artist_nationality\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "local_impact_japan", "columns": null}, {"table": "tracks", "columns": null}], "writes": [{"table": "bi.events_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.causeid > 150).all()\n# src table: bi.bi_risk_score_delta\nengine.execute(\"INSERT INTO ods_products_delta SELECT * FROM bi.bi_risk_score_delta\")\n", "labels": {"reads": [{"table": "bi.bi_risk_score_delta", "columns": null}], "writes": [{"table": "ods_products_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cosmetic_formula SELECT amount_claimed, gender_mf, species_name FROM volunteer_registration WHERE amount_claimed > 214\"], check=True)\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": ["amount_claimed", "gender_mf", "species_name"]}], "writes": [{"table": "cosmetic_formula", "columns": ["amount_claimed", "gender_mf", "species_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO ap_budget SELECT exhibitioncountry, color, month, meal_date FROM documents WHERE exhibitioncountry > 109\"\n", "labels": {"reads": [{"table": "documents", "columns": ["exhibitioncountry", "color", "month", "meal_date"]}], "writes": [{"table": "ap_budget", "columns": ["exhibitioncountry", "color", "month", "meal_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_in_locaton_to, testtypeid FROM nailpolishsales\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"armed_forces\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "nailpolishsales", "columns": ["date_in_locaton_to", "testtypeid"]}], "writes": [{"table": "armed_forces", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO emergencies SELECT city_traffic_speed, council_id, is_electric FROM flight_safety WHERE city_traffic_speed > 301\"\n", "labels": {"reads": [{"table": "flight_safety", "columns": ["city_traffic_speed", "council_id", "is_electric"]}], "writes": [{"table": "emergencies", "columns": ["city_traffic_speed", "council_id", "is_electric"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT exhibition_name, initiative_type FROM clothing_brands LIMIT 25;\nINSERT INTO whale_sharks SELECT vulnerability_name, product_subcategory, handling_id FROM broadband_revenue WHERE vulnerability_name > 74;\nSQL\n", "labels": {"reads": [{"table": "clothing_brands", "columns": ["exhibition_name", "initiative_type"]}, {"table": "broadband_revenue", "columns": ["vulnerability_name", "product_subcategory", "handling_id"]}], "writes": [{"table": "whale_sharks", "columns": ["vulnerability_name", "product_subcategory", "handling_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM phone\", conn)\ndf.to_sql(\"hall_of_fame\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "phone", "columns": null}], "writes": [{"table": "hall_of_fame", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods.ods_risk_score_df (killed, student_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_risk_score_df", "columns": ["killed", "student_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO mart.mart_device_log_delta SELECT register_year, device, claimamount, hours_developed FROM mart.campaigns_full WHERE register_year > 320\")\n", "labels": {"reads": [{"table": "mart.campaigns_full", "columns": ["register_year", "device", "claimamount", "hours_developed"]}], "writes": [{"table": "mart.mart_device_log_delta", "columns": ["register_year", "device", "claimamount", "hours_developed"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO energy_production SELECT build_year, suppliername, event_name, yield_id FROM flu_cases WHERE build_year > 190\"\n", "labels": {"reads": [{"table": "flu_cases", "columns": ["build_year", "suppliername", "event_name", "yield_id"]}], "writes": [{"table": "energy_production", "columns": ["build_year", "suppliername", "event_name", "yield_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table fashion_trend_data --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"has_allergy\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "has_allergy", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT capacity_percentage, deliveryid FROM virtual_visitors LIMIT 433\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO policy_feedback SELECT cropid, claim_stage_id FROM public_works_projects WHERE cropid > 293\")\n", "labels": {"reads": [{"table": "virtual_visitors", "columns": ["capacity_percentage", "deliveryid"]}, {"table": "public_works_projects", "columns": ["cropid", "claim_stage_id"]}], "writes": [{"table": "policy_feedback", "columns": ["cropid", "claim_stage_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cybersecuritybudget SELECT * FROM legacy\ncur.execute(\"SELECT exhibition_name, production FROM show LIMIT 448\")\n", "labels": {"reads": [{"table": "show", "columns": ["exhibition_name", "production"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM climate_finance_asia\", conn)\ndf.to_sql(\"communitycourts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": null}], "writes": [{"table": "communitycourts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO exhibition_visitors SELECT userid, last_maintenance FROM workplaces WHERE userid > 35\"\n", "labels": {"reads": [{"table": "workplaces", "columns": ["userid", "last_maintenance"]}], "writes": [{"table": "exhibition_visitors", "columns": ["userid", "last_maintenance"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table buildingpermits --target-dir /tmp/land\n", "labels": {"reads": [{"table": "buildingpermits", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donationid, route FROM immunizationrates LIMIT 478\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "immunizationrates", "columns": ["donationid", "route"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mediators SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO agri_innovations SELECT * FROM legacy\ncur.execute(\"SELECT horizontal_bar_points, communityname FROM district LIMIT 316\")\n", "labels": {"reads": [{"table": "district", "columns": ["horizontal_bar_points", "communityname"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO member_data SELECT * FROM legacy\ncur.execute(\"SELECT fouls, discount FROM humanitarian_aid LIMIT 158\")\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["fouls", "discount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO timber_production SELECT budget_allocation, course_name FROM visitor_statistics WHERE budget_allocation > 409\"\n", "labels": {"reads": [{"table": "visitor_statistics", "columns": ["budget_allocation", "course_name"]}], "writes": [{"table": "timber_production", "columns": ["budget_allocation", "course_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT certification, anomaly FROM gender LIMIT 313;\nINSERT INTO stellar_transactions SELECT energy_efficiency_savings, roomid, department_id FROM genre_songs WHERE energy_efficiency_savings > 22;\nSQL\n", "labels": {"reads": [{"table": "gender", "columns": ["certification", "anomaly"]}, {"table": "genre_songs", "columns": ["energy_efficiency_savings", "roomid", "department_id"]}], "writes": [{"table": "stellar_transactions", "columns": ["energy_efficiency_savings", "roomid", "department_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"cases\")\nexport_to_sink(df, \"vehicle_sales\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "cases", "columns": null}], "writes": [{"table": "vehicle_sales", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO coralreefs SELECT replacement_cost, courtname, address_details, trainingdate FROM construction_labor_stats WHERE replacement_cost > 74\")\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": ["replacement_cost", "courtname", "address_details", "trainingdate"]}], "writes": [{"table": "coralreefs", "columns": ["replacement_cost", "courtname", "address_details", "trainingdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_orders_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"concert_revenue\")\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": null}], "writes": [{"table": "concert_revenue", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO healthcare_centers SELECT * FROM legacy\ncur.execute(\"SELECT plant_id, contract_id FROM underwater_cables LIMIT 63\")\n", "labels": {"reads": [{"table": "underwater_cables", "columns": ["plant_id", "contract_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO plays_games SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT datetime_detention_start, transaction_product FROM digital_divide_initiatives LIMIT 227;\nINSERT INTO fans SELECT testtypeid, laborproductivity, date_to FROM stg.stg_inventory_full WHERE testtypeid > 3;\nSQL\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": ["datetime_detention_start", "transaction_product"]}, {"table": "stg.stg_inventory_full", "columns": ["testtypeid", "laborproductivity", "date_to"]}], "writes": [{"table": "fans", "columns": ["testtypeid", "laborproductivity", "date_to"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO workforce_development_programs SELECT * FROM legacy\ncur.execute(\"SELECT asset_details, deliverydate FROM cultural_competency_training LIMIT 8\")\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": ["asset_details", "deliverydate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO financial_transactions SELECT cuisine, school_code FROM weekly_weather WHERE cuisine > 422;\nEOF\n", "labels": {"reads": [{"table": "weekly_weather", "columns": ["cuisine", "school_code"]}], "writes": [{"table": "financial_transactions", "columns": ["cuisine", "school_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"team\")\nsrc.write.insertInto(\"militarypersonnel\", overwrite=True)\n", "labels": {"reads": [{"table": "team", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT contactid, volunteerhourid FROM researchpapers LIMIT 165;\nINSERT INTO medicine SELECT degrees, assists, min_dew_point_f, cmi_cross_ref_id FROM public.forest_stats WHERE degrees > 332;\nSQL\n", "labels": {"reads": [{"table": "researchpapers", "columns": ["contactid", "volunteerhourid"]}, {"table": "public.forest_stats", "columns": ["degrees", "assists", "min_dew_point_f", "cmi_cross_ref_id"]}], "writes": [{"table": "medicine", "columns": ["degrees", "assists", "min_dew_point_f", "cmi_cross_ref_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT event_type_id, student_id FROM rural_clinics\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"dws.payments_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "rural_clinics", "columns": ["event_type_id", "student_id"]}], "writes": [{"table": "dws.payments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO features SELECT discovered_date, launch_date, department_id, studentid FROM bi_device_log_daily WHERE discovered_date > 274\")\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": ["discovered_date", "launch_date", "department_id", "studentid"]}], "writes": [{"table": "features", "columns": ["discovered_date", "launch_date", "department_id", "studentid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table festivals --target-dir /tmp/land\n", "labels": {"reads": [{"table": "festivals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO concentrateprices SELECT system, all_games, ota_name, coach_name FROM higher_ed.publications WHERE system > 120\"\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": ["system", "all_games", "ota_name", "coach_name"]}], "writes": [{"table": "concentrateprices", "columns": ["system", "all_games", "ota_name", "coach_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cybersecurity.strategies SELECT spacecraft_id, observation_id FROM degrees WHERE spacecraft_id > 23\"], check=True)\n", "labels": {"reads": [{"table": "degrees", "columns": ["spacecraft_id", "observation_id"]}], "writes": [{"table": "cybersecurity.strategies", "columns": ["spacecraft_id", "observation_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO third_party_companies (total_beds, production) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "third_party_companies", "columns": ["total_beds", "production"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"road_construction\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "road_construction", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO web_client_accelerator SELECT donorname, ranking FROM seeds WHERE donorname > 134\"], check=True)\n", "labels": {"reads": [{"table": "seeds", "columns": ["donorname", "ranking"]}], "writes": [{"table": "web_client_accelerator", "columns": ["donorname", "ranking"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT end_station_id, founder_gender FROM engineer_visits LIMIT 192;\nINSERT INTO mart.mart_coupon_use_full SELECT recruiterid, outcome_description, sales_in_billion FROM canada_tech WHERE recruiterid > 246;\nSQL\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["end_station_id", "founder_gender"]}, {"table": "canada_tech", "columns": ["recruiterid", "outcome_description", "sales_in_billion"]}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": ["recruiterid", "outcome_description", "sales_in_billion"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"classrooms\")\nsrc.write.insertInto(\"artists\", overwrite=True)\n", "labels": {"reads": [{"table": "classrooms", "columns": null}], "writes": [{"table": "artists", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table stg_orders_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_education_programs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"districts_india\")\n", "labels": {"reads": [{"table": "community_education_programs", "columns": null}], "writes": [{"table": "districts_india", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO rating SELECT date_became_customer, fair_trade, budgeted FROM product_catalog WHERE date_became_customer > 369\"\n", "labels": {"reads": [{"table": "product_catalog", "columns": ["date_became_customer", "fair_trade", "budgeted"]}], "writes": [{"table": "rating", "columns": ["date_became_customer", "fair_trade", "budgeted"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table ratings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd.dwd_payments_full SELECT premise_id, city_name FROM dams WHERE premise_id > 27\"], check=True)\n", "labels": {"reads": [{"table": "dams", "columns": ["premise_id", "city_name"]}], "writes": [{"table": "dwd.dwd_payments_full", "columns": ["premise_id", "city_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table justice_schemas.legal_tech_providers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recycled_polyester SELECT * FROM legacy\ncur.execute(\"SELECT funding_round_id, day_of_week FROM market_share LIMIT 349\")\n", "labels": {"reads": [{"table": "market_share", "columns": ["funding_round_id", "day_of_week"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO rural_hospitals SELECT is_ev, menu_item, allocation_date, production_budget FROM reservations WHERE is_ev > 64\"\n", "labels": {"reads": [{"table": "reservations", "columns": ["is_ev", "menu_item", "allocation_date", "production_budget"]}], "writes": [{"table": "rural_hospitals", "columns": ["is_ev", "menu_item", "allocation_date", "production_budget"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO community_policing SELECT amount_of_refund, purchase_details FROM iot_sensors WHERE amount_of_refund > 27;\nEOF\n", "labels": {"reads": [{"table": "iot_sensors", "columns": ["amount_of_refund", "purchase_details"]}], "writes": [{"table": "community_policing", "columns": ["amount_of_refund", "purchase_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO art_pieces SELECT funder, authid, hotel_chain_name, billingamount FROM ais WHERE funder > 149\")\n", "labels": {"reads": [{"table": "ais", "columns": ["funder", "authid", "hotel_chain_name", "billingamount"]}], "writes": [{"table": "art_pieces", "columns": ["funder", "authid", "hotel_chain_name", "billingamount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table arctictemperature --target-dir /tmp/land\n", "labels": {"reads": [{"table": "arctictemperature", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO marine_species_observations SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"screen_mode\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "screen_mode", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO security_incidents SELECT specialty, max_age FROM mart.mart_sessions_di WHERE specialty > 120\"\n", "labels": {"reads": [{"table": "mart.mart_sessions_di", "columns": ["specialty", "max_age"]}], "writes": [{"table": "security_incidents", "columns": ["specialty", "max_age"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO apartment_bookings SELECT tripdatetime, facid, artworkid FROM patient_satisfaction WHERE tripdatetime > 284\"], check=True)\n", "labels": {"reads": [{"table": "patient_satisfaction", "columns": ["tripdatetime", "facid", "artworkid"]}], "writes": [{"table": "apartment_bookings", "columns": ["tripdatetime", "facid", "artworkid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM arctic_marine_species\", conn)\ndf.to_sql(\"therapists\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": null}], "writes": [{"table": "therapists", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dailystreams SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO recycling_stats (field_name, programid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "recycling_stats", "columns": ["field_name", "programid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.don_name > 195).all()\n# src table: rental\nengine.execute(\"INSERT INTO intelligenceoperations SELECT * FROM rental\")\n", "labels": {"reads": [{"table": "rental", "columns": null}], "writes": [{"table": "intelligenceoperations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT premise_details, complaint_status_code FROM visual_arts LIMIT 353;\nINSERT INTO organicproducts SELECT leader_name, sent_date, cost FROM biosensors.patents WHERE leader_name > 74;\nSQL\n", "labels": {"reads": [{"table": "visual_arts", "columns": ["premise_details", "complaint_status_code"]}, {"table": "biosensors.patents", "columns": ["leader_name", "sent_date", "cost"]}], "writes": [{"table": "organicproducts", "columns": ["leader_name", "sent_date", "cost"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"trains\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"students\")\n", "labels": {"reads": [{"table": "trains", "columns": null}], "writes": [{"table": "students", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ref_document_status\", conn)\ndf.to_sql(\"assessment_notes\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ref_document_status", "columns": null}], "writes": [{"table": "assessment_notes", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model courts depends on machines\ndbt run --select courts --vars '{\"source_table\":\"machines\"}'\n", "labels": {"reads": [{"table": "machines", "columns": null}], "writes": [{"table": "courts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd_sessions_hourly SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.sessions SELECT daily_consumption, reporter_id FROM mediators WHERE daily_consumption > 86;\nEOF\n", "labels": {"reads": [{"table": "mediators", "columns": ["daily_consumption", "reporter_id"]}], "writes": [{"table": "ods.sessions", "columns": ["daily_consumption", "reporter_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT program, min_salary FROM bi.clicks_hourly LIMIT 381;\nINSERT INTO training SELECT vehicle_details, attendee_race, system, casestatus FROM bike_share WHERE vehicle_details > 127;\nSQL\n", "labels": {"reads": [{"table": "bi.clicks_hourly", "columns": ["program", "min_salary"]}, {"table": "bike_share", "columns": ["vehicle_details", "attendee_race", "system", "casestatus"]}], "writes": [{"table": "training", "columns": ["vehicle_details", "attendee_race", "system", "casestatus"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO labor_statistics SELECT formats, stop, co2_emission, policyholder_id FROM check_ins WHERE formats > 92\"], check=True)\n", "labels": {"reads": [{"table": "check_ins", "columns": ["formats", "stop", "co2_emission", "policyholder_id"]}], "writes": [{"table": "labor_statistics", "columns": ["formats", "stop", "co2_emission", "policyholder_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT market_rate, spill_name FROM document_structures\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"visualartprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "document_structures", "columns": ["market_rate", "spill_name"]}], "writes": [{"table": "visualartprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO hospitallocations SELECT a.disability_type, b.faculty FROM investment a JOIN player_coach b ON a.bname = b.bname\"\n", "labels": {"reads": [{"table": "investment", "columns": null}, {"table": "player_coach", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT charging_level, publication_id FROM project_issues\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ndf.to_sql(\"weather_record\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "project_issues", "columns": ["charging_level", "publication_id"]}], "writes": [{"table": "weather_record", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT person_id, vesselid FROM voyages LIMIT 334\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "voyages", "columns": ["person_id", "vesselid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO satelliteimagery SELECT permits_issued, tourist_id FROM energy_production WHERE permits_issued > 500\")\n", "labels": {"reads": [{"table": "energy_production", "columns": ["permits_issued", "tourist_id"]}], "writes": [{"table": "satelliteimagery", "columns": ["permits_issued", "tourist_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tree_species SELECT * FROM legacy\ncur.execute(\"SELECT roomname, publicationid FROM satellites LIMIT 290\")\n", "labels": {"reads": [{"table": "satellites", "columns": ["roomname", "publicationid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO social_good_education SELECT builder, certified FROM violations WHERE builder > 328;\nEOF\n", "labels": {"reads": [{"table": "violations", "columns": ["builder", "certified"]}], "writes": [{"table": "social_good_education", "columns": ["builder", "certified"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trade_name, contractorid FROM iot_sensors LIMIT 22\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "iot_sensors", "columns": ["trade_name", "contractorid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_power\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ai_ethics\")\n", "labels": {"reads": [{"table": "renewable_power", "columns": null}], "writes": [{"table": "ai_ethics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"commercialbuildings\")\nsrc.write.insertInto(\"job_postings\", overwrite=True)\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": null}], "writes": [{"table": "job_postings", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT feature_details, matchdate FROM animal_species LIMIT 473\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "animal_species", "columns": ["feature_details", "matchdate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model safe_dataset depends on video_games\ndbt run --models safe_dataset --vars 'source: video_games'\n", "labels": {"reads": [{"table": "video_games", "columns": null}], "writes": [{"table": "safe_dataset", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mining_operation_data SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_energy_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"races\")\n", "labels": {"reads": [{"table": "renewable_energy_projects", "columns": null}], "writes": [{"table": "races", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT plant, claimtype FROM royal_family LIMIT 375;\nINSERT INTO attack_outcomes SELECT played, total, mission FROM policy_feedback WHERE played > 304;\nSQL\n", "labels": {"reads": [{"table": "royal_family", "columns": ["plant", "claimtype"]}, {"table": "policy_feedback", "columns": ["played", "total", "mission"]}], "writes": [{"table": "attack_outcomes", "columns": ["played", "total", "mission"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT distance, author_or_editor FROM forest LIMIT 18;\nINSERT INTO bi.bi_risk_score_delta SELECT menuitemid, bandmate, male_id, school_name FROM community_health_workers WHERE menuitemid > 418;\nSQL\n", "labels": {"reads": [{"table": "forest", "columns": ["distance", "author_or_editor"]}, {"table": "community_health_workers", "columns": ["menuitemid", "bandmate", "male_id", "school_name"]}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["menuitemid", "bandmate", "male_id", "school_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table construction_union --columns projecttype,startup_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "construction_union", "columns": ["projecttype", "startup_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO excavation SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd_sessions_df (machine, mining_operation) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd_sessions_df", "columns": ["machine", "mining_operation"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.application_date > 486).all()\n# src table: tours\nengine.execute(\"INSERT INTO timber_production SELECT * FROM tours\")\n", "labels": {"reads": [{"table": "tours", "columns": null}], "writes": [{"table": "timber_production", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd.dwd_inventory_hourly SELECT line_1, characteristic_id, unit_id, game_id FROM healthcare_centers WHERE line_1 > 144\"\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": ["line_1", "characteristic_id", "unit_id", "game_id"]}], "writes": [{"table": "dwd.dwd_inventory_hourly", "columns": ["line_1", "characteristic_id", "unit_id", "game_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT programtype, policy_type FROM outcomes LIMIT 392;\nINSERT INTO offender_demographics SELECT provider_name, graphics_mode, build_date, researcher_name FROM gardens WHERE provider_name > 206;\nSQL\n", "labels": {"reads": [{"table": "outcomes", "columns": ["programtype", "policy_type"]}, {"table": "gardens", "columns": ["provider_name", "graphics_mode", "build_date", "researcher_name"]}], "writes": [{"table": "offender_demographics", "columns": ["provider_name", "graphics_mode", "build_date", "researcher_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO time_dim SELECT bioprocess_name, dept_name, working_year_starts, water_temp FROM dws.inventory_daily WHERE bioprocess_name > 371\"\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": ["bioprocess_name", "dept_name", "working_year_starts", "water_temp"]}], "writes": [{"table": "time_dim", "columns": ["bioprocess_name", "dept_name", "working_year_starts", "water_temp"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model diversity depends on neighborhoods\ndbt run -s diversity --vars '{\"source_table\":\"neighborhoods\"}'\n", "labels": {"reads": [{"table": "neighborhoods", "columns": null}], "writes": [{"table": "diversity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO esportsevents SELECT a.ai_algorithm_id, b.doctor_id FROM community_members a JOIN shops b ON a.date_order_placed = b.date_order_placed\"\n", "labels": {"reads": [{"table": "community_members", "columns": null}, {"table": "shops", "columns": null}], "writes": [{"table": "esportsevents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table socially_responsible_loans --target-dir /tmp/land\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO disease_prevalence SELECT chemical_type, program_id, meter_100, station_name FROM customer_master_index WHERE chemical_type > 80\"\n", "labels": {"reads": [{"table": "customer_master_index", "columns": ["chemical_type", "program_id", "meter_100", "station_name"]}], "writes": [{"table": "disease_prevalence", "columns": ["chemical_type", "program_id", "meter_100", "station_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_renewable_energy\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"animal_population_status\")\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": null}], "writes": [{"table": "animal_population_status", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads.ads_exposure_di SELECT shipmentid, dish_type, structure_type FROM productsafety WHERE shipmentid > 152;\nEOF\n", "labels": {"reads": [{"table": "productsafety", "columns": ["shipmentid", "dish_type", "structure_type"]}], "writes": [{"table": "ads.ads_exposure_di", "columns": ["shipmentid", "dish_type", "structure_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ai_id, seal_species FROM investors LIMIT 209\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "investors", "columns": ["ai_id", "seal_species"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_cart_item_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"public.crime_types\")\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": null}], "writes": [{"table": "public.crime_types", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"dws.dws_member_point_df\")\ndump_to_store(df, \"representative\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": null}], "writes": [{"table": "representative", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ticketspending SELECT killed, compatible_since_year FROM sustainable_urban_properties_2 WHERE killed > 211\"\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": ["killed", "compatible_since_year"]}], "writes": [{"table": "ticketspending", "columns": ["killed", "compatible_since_year"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO player_demographics SELECT investment_id, plant FROM vulnerabilities WHERE investment_id > 350\"], check=True)\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": ["investment_id", "plant"]}], "writes": [{"table": "player_demographics", "columns": ["investment_id", "plant"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO fish_suppliers SELECT chemicalid, bike_id, mean_sea_level_pressure_inches, device_id FROM elimination WHERE chemicalid > 73\"\n", "labels": {"reads": [{"table": "elimination", "columns": ["chemicalid", "bike_id", "mean_sea_level_pressure_inches", "device_id"]}], "writes": [{"table": "fish_suppliers", "columns": ["chemicalid", "bike_id", "mean_sea_level_pressure_inches", "device_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT vendor_state, bats FROM concert LIMIT 68;\nINSERT INTO marketing_budgets SELECT supplier_id, num_investments FROM bi.bi_campaigns_delta WHERE supplier_id > 353;\nSQL\n", "labels": {"reads": [{"table": "concert", "columns": ["vendor_state", "bats"]}, {"table": "bi.bi_campaigns_delta", "columns": ["supplier_id", "num_investments"]}], "writes": [{"table": "marketing_budgets", "columns": ["supplier_id", "num_investments"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT population, project_name FROM field5 LIMIT 234\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "field5", "columns": ["population", "project_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model program_history depends on authors\ndbt build --select program_history --vars '{\"src\":\"authors\"}'\n", "labels": {"reads": [{"table": "authors", "columns": null}], "writes": [{"table": "program_history", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"brandrevenue\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "brandrevenue", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table artprograms --target-dir /tmp/land\n", "labels": {"reads": [{"table": "artprograms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"farms\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "farms", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT museum_details, peakhourid FROM sports_events LIMIT 154;\nINSERT INTO textile_waste SELECT total_attendance, preferred_foot, market_value_in_billion, taskdate FROM foodsafetyrecords WHERE total_attendance > 4;\nSQL\n", "labels": {"reads": [{"table": "sports_events", "columns": ["museum_details", "peakhourid"]}, {"table": "foodsafetyrecords", "columns": ["total_attendance", "preferred_foot", "market_value_in_billion", "taskdate"]}], "writes": [{"table": "textile_waste", "columns": ["total_attendance", "preferred_foot", "market_value_in_billion", "taskdate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainablebrands SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fare_segments\", conn)\ndf.to_sql(\"parties\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "parties", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"energy_prices\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"status\")\n", "labels": {"reads": [{"table": "energy_prices", "columns": null}], "writes": [{"table": "status", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"autonomousvehicles\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"settlements\")\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": null}], "writes": [{"table": "settlements", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"safetytests\")\nsrc.write.insertInto(\"flight_emissions\", overwrite=True)\n", "labels": {"reads": [{"table": "safetytests", "columns": null}], "writes": [{"table": "flight_emissions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO gardens SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.risk_score_hourly (donor_id, user_category) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.risk_score_hourly", "columns": ["donor_id", "user_category"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sales_quarterly\").toPandas()\ndf[[\"production_budget\", \"num_cases\"]].to_sql(\"green_buildings_us\", engine, index=False)\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": null}], "writes": [{"table": "green_buildings_us", "columns": ["production_budget", "num_cases"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"units\")\nsrc.write.insertInto(\"ods.ods_clicks_di\", overwrite=True)\n", "labels": {"reads": [{"table": "units", "columns": null}], "writes": [{"table": "ods.ods_clicks_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attendee_demographics\").toPandas()\ndf[[\"saledate\", \"log_entry_description\"]].to_sql(\"waterconservation\", engine, index=False)\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": null}], "writes": [{"table": "waterconservation", "columns": ["saledate", "log_entry_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"products_in_events\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ancient_cultures\")\n", "labels": {"reads": [{"table": "products_in_events", "columns": null}], "writes": [{"table": "ancient_cultures", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO program_history (attendee_id, user_account) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "program_history", "columns": ["attendee_id", "user_account"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model grad_students depends on russia_nato_diplomacy\ndbt run --models grad_students --vars '{\"source_table\":\"russia_nato_diplomacy\"}'\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": [{"table": "grad_students", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO city_properties SELECT time_id, donor_id FROM attack_outcomes WHERE time_id > 263\"], check=True)\n", "labels": {"reads": [{"table": "attack_outcomes", "columns": ["time_id", "donor_id"]}], "writes": [{"table": "city_properties", "columns": ["time_id", "donor_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO container_ships SELECT co2_reduction_tons, media_literacy_score FROM weekly_weather WHERE co2_reduction_tons > 469\"\n", "labels": {"reads": [{"table": "weekly_weather", "columns": ["co2_reduction_tons", "media_literacy_score"]}], "writes": [{"table": "container_ships", "columns": ["co2_reduction_tons", "media_literacy_score"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table supplier_addresses --columns practices,trial_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "supplier_addresses", "columns": ["practices", "trial_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO artsandcrafts SELECT budget_type_description, connection, eventname, filingdate FROM flu_cases WHERE budget_type_description > 447\")\n", "labels": {"reads": [{"table": "flu_cases", "columns": ["budget_type_description", "connection", "eventname", "filingdate"]}], "writes": [{"table": "artsandcrafts", "columns": ["budget_type_description", "connection", "eventname", "filingdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM diversity_metrics\"\n", "labels": {"reads": [{"table": "diversity_metrics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"park\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "park", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT num_solo_exhibitions, views FROM upgrades\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"public.ev_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "upgrades", "columns": ["num_solo_exhibitions", "views"]}], "writes": [{"table": "public.ev_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO flights SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM units\", conn)\ndf.to_sql(\"fleet_management\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "units", "columns": null}], "writes": [{"table": "fleet_management", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table smart_contracts_transactions --columns galleryname,year_join --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "smart_contracts_transactions", "columns": ["galleryname", "year_join"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reaction_time, range FROM canals LIMIT 197\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "canals", "columns": ["reaction_time", "range"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"shops\")\nsrc.write.insertInto(\"mammals\", overwrite=True)\n", "labels": {"reads": [{"table": "shops", "columns": null}], "writes": [{"table": "mammals", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_risk_score_df SELECT * FROM legacy\ncur.execute(\"SELECT dribbling, company_gender FROM attorneylocationyear LIMIT 459\")\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": ["dribbling", "company_gender"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO premises SELECT transactions, gamesplayed, capacity_percentage, production_quantity FROM dw.dw_inventory_df WHERE transactions > 230\"\n", "labels": {"reads": [{"table": "dw.dw_inventory_df", "columns": ["transactions", "gamesplayed", "capacity_percentage", "production_quantity"]}], "writes": [{"table": "premises", "columns": ["transactions", "gamesplayed", "capacity_percentage", "production_quantity"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO subway_stations_seoul SELECT a.bridgetype, b.unitsperweek FROM issues a JOIN mexico_regions b ON a.countid = b.countid\"\n", "labels": {"reads": [{"table": "issues", "columns": null}, {"table": "mexico_regions", "columns": null}], "writes": [{"table": "subway_stations_seoul", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM staff_roles\", conn)\ndf.to_sql(\"ocean_health_monitor\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "staff_roles", "columns": null}], "writes": [{"table": "ocean_health_monitor", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"conservation_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "conservation_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.coach_id > 427).all()\n# src table: preferences\nengine.execute(\"INSERT INTO government.region SELECT * FROM preferences\")\n", "labels": {"reads": [{"table": "preferences", "columns": null}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"electric_vehicle_stats\").toPandas()\ndf[[\"virtual_tour_engagement_time\", \"census_ranking\"]].to_sql(\"communitypolicingcenters\", engine, index=False)\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": [{"table": "communitypolicingcenters", "columns": ["virtual_tour_engagement_time", "census_ranking"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO gardens SELECT clean_jerk, artifactname, response_time FROM recycledmaterialsgarments WHERE clean_jerk > 64\")\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": ["clean_jerk", "artifactname", "response_time"]}], "writes": [{"table": "gardens", "columns": ["clean_jerk", "artifactname", "response_time"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecurity.strategies\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cybersecurity.strategies", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"restorative_justice_programs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"whale_sightings\")\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": null}], "writes": [{"table": "whale_sightings", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artprograms\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.events_delta\")\n", "labels": {"reads": [{"table": "artprograms", "columns": null}], "writes": [{"table": "bi.events_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO research SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dwd_coupon_use_hourly depends on ethical_ai\ndbt build --models dwd_coupon_use_hourly --vars 'source: ethical_ai'\n", "labels": {"reads": [{"table": "ethical_ai", "columns": null}], "writes": [{"table": "dwd_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_ethics_policies\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT provider_id, accessible FROM takes LIMIT 448;\nINSERT INTO textileworkers SELECT acidity, opname FROM address WHERE acidity > 186;\nSQL\n", "labels": {"reads": [{"table": "takes", "columns": ["provider_id", "accessible"]}, {"table": "address", "columns": ["acidity", "opname"]}], "writes": [{"table": "textileworkers", "columns": ["acidity", "opname"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"militarybases\")\nsrc.write.insertInto(\"euro_champs_track_field\", overwrite=True)\n", "labels": {"reads": [{"table": "militarybases", "columns": null}], "writes": [{"table": "euro_champs_track_field", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM makeup_products\"\n", "labels": {"reads": [{"table": "makeup_products", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"donations\")\npush_to_warehouse(df, \"water_sources\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "donations", "columns": null}], "writes": [{"table": "water_sources", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO crops SELECT artwork_id, hiv, emp_hiredate FROM high_risk WHERE artwork_id > 66\"\n", "labels": {"reads": [{"table": "high_risk", "columns": ["artwork_id", "hiv", "emp_hiredate"]}], "writes": [{"table": "crops", "columns": ["artwork_id", "hiv", "emp_hiredate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemicals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "chemicals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"esports_teams\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"management\")\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}], "writes": [{"table": "management", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM extraction_methods\", conn)\ndf.to_sql(\"tree_types\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "extraction_methods", "columns": null}], "writes": [{"table": "tree_types", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"indian_ocean_wells\").toPandas()\ndf[[\"approach\", \"medication\"]].to_sql(\"multimodalhubs\", engine, index=False)\n", "labels": {"reads": [{"table": "indian_ocean_wells", "columns": null}], "writes": [{"table": "multimodalhubs", "columns": ["approach", "medication"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"initiative_types\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.bi_orders_daily\")\n", "labels": {"reads": [{"table": "initiative_types", "columns": null}], "writes": [{"table": "bi.bi_orders_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads_users_hourly SELECT has_disability, last_updated FROM levees WHERE has_disability > 92\"\n", "labels": {"reads": [{"table": "levees", "columns": ["has_disability", "last_updated"]}], "writes": [{"table": "ads_users_hourly", "columns": ["has_disability", "last_updated"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_feeder_roads\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.mart_sessions_di SELECT device_name, date_from FROM assets WHERE device_name > 400\"\n", "labels": {"reads": [{"table": "assets", "columns": ["device_name", "date_from"]}], "writes": [{"table": "mart.mart_sessions_di", "columns": ["device_name", "date_from"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO pilot SELECT initiative_name, organization_id, ai_adoption_date, channel_code FROM visits_restaurant WHERE initiative_name > 478\")\n", "labels": {"reads": [{"table": "visits_restaurant", "columns": ["initiative_name", "organization_id", "ai_adoption_date", "channel_code"]}], "writes": [{"table": "pilot", "columns": ["initiative_name", "organization_id", "ai_adoption_date", "channel_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO equipment SELECT attendees, fare_id FROM vessel_safety WHERE attendees > 126\"\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["attendees", "fare_id"]}], "writes": [{"table": "equipment", "columns": ["attendees", "fare_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart_exposure_di SELECT * FROM legacy\ncur.execute(\"SELECT forename, refugee_name FROM gamereviews LIMIT 309\")\n", "labels": {"reads": [{"table": "gamereviews", "columns": ["forename", "refugee_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"instructors\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"militaryinnovations\")\n", "labels": {"reads": [{"table": "instructors", "columns": null}], "writes": [{"table": "militaryinnovations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO streams SELECT duration, crime_type, defense_contractor_id, bathroom_count FROM news_report WHERE duration > 134;\nEOF\n", "labels": {"reads": [{"table": "news_report", "columns": ["duration", "crime_type", "defense_contractor_id", "bathroom_count"]}], "writes": [{"table": "streams", "columns": ["duration", "crime_type", "defense_contractor_id", "bathroom_count"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT publication_id, num_beds FROM mart.mart_users_di LIMIT 406\")\nimport logging\nspark.sql(\"INSERT INTO thefttypes SELECT yield_id, open_date, socially_responsible, project_type FROM stg.stg_risk_score_hourly WHERE yield_id > 156\")\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": ["publication_id", "num_beds"]}, {"table": "stg.stg_risk_score_hourly", "columns": ["yield_id", "open_date", "socially_responsible", "project_type"]}], "writes": [{"table": "thefttypes", "columns": ["yield_id", "open_date", "socially_responsible", "project_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO electric_vehicles SELECT underrepresented_community, method_name, equipment_name FROM sitem WHERE underrepresented_community > 201\"\n", "labels": {"reads": [{"table": "sitem", "columns": ["underrepresented_community", "method_name", "equipment_name"]}], "writes": [{"table": "electric_vehicles", "columns": ["underrepresented_community", "method_name", "equipment_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO assets_frameworks SELECT treatment_id, minesite FROM seamounts WHERE treatment_id > 80\"\n", "labels": {"reads": [{"table": "seamounts", "columns": ["treatment_id", "minesite"]}], "writes": [{"table": "assets_frameworks", "columns": ["treatment_id", "minesite"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO obesity SELECT birth_place, brand_mentioned FROM mappinglengths WHERE birth_place > 95\"], check=True)\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["birth_place", "brand_mentioned"]}], "writes": [{"table": "obesity", "columns": ["birth_place", "brand_mentioned"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT bathroom_count, hours_developed FROM consumer\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"disinformation_detection\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "consumer", "columns": ["bathroom_count", "hours_developed"]}], "writes": [{"table": "disinformation_detection", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT city_population, shipment_id FROM diversity LIMIT 213;\nINSERT INTO carbon_offsets.carbon_offsets SELECT framework_id, last_name, lot_details FROM donation WHERE framework_id > 151;\nSQL\n", "labels": {"reads": [{"table": "diversity", "columns": ["city_population", "shipment_id"]}, {"table": "donation", "columns": ["framework_id", "last_name", "lot_details"]}], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": ["framework_id", "last_name", "lot_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_refunds SELECT shipmenttype, item_size, tree_id, performancedate FROM diplomacy_events WHERE shipmenttype > 479\"], check=True)\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": ["shipmenttype", "item_size", "tree_id", "performancedate"]}], "writes": [{"table": "mart_refunds", "columns": ["shipmenttype", "item_size", "tree_id", "performancedate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO atlantic_plate SELECT * FROM legacy\ncur.execute(\"SELECT songid, phone_number FROM clinics LIMIT 139\")\n", "labels": {"reads": [{"table": "clinics", "columns": ["songid", "phone_number"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO singer SELECT a.running_time, b.scientist FROM florida_conservation_initiatives a JOIN marine_species_arctic_ocean b ON a.asset_acquired_date = b.asset_acquired_date\"\n", "labels": {"reads": [{"table": "florida_conservation_initiatives", "columns": null}, {"table": "marine_species_arctic_ocean", "columns": null}], "writes": [{"table": "singer", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO missions SELECT made_in_usa, delivery_time FROM ads_exposure_hourly WHERE made_in_usa > 310\")\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["made_in_usa", "delivery_time"]}], "writes": [{"table": "missions", "columns": ["made_in_usa", "delivery_time"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"biosensors\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_member_point_di\")\n", "labels": {"reads": [{"table": "biosensors", "columns": null}], "writes": [{"table": "dws.dws_member_point_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ethicalaibudget\"\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO digitalliteracytraining SELECT a.allergy, b.num_owners FROM assets_frameworks a JOIN elimination b ON a.donation_id = b.donation_id\"\n", "labels": {"reads": [{"table": "assets_frameworks", "columns": null}, {"table": "elimination", "columns": null}], "writes": [{"table": "digitalliteracytraining", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"business_rates\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws_coupon_use\")\n", "labels": {"reads": [{"table": "business_rates", "columns": null}], "writes": [{"table": "dws_coupon_use", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO dw.shipments_df SELECT a.thefttypeid, b.component_name FROM mental_health_parity_violations a JOIN artists_valuation b ON a.total = b.total\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}, {"table": "artists_valuation", "columns": null}], "writes": [{"table": "dw.shipments_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table department_publications --target-dir /tmp/land\n", "labels": {"reads": [{"table": "department_publications", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.orders_daily SELECT contractor, consultations, purchase_date, problem_id FROM community_events WHERE contractor > 245\"\n", "labels": {"reads": [{"table": "community_events", "columns": ["contractor", "consultations", "purchase_date", "problem_id"]}], "writes": [{"table": "stg.orders_daily", "columns": ["contractor", "consultations", "purchase_date", "problem_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT headquartered_city, access_count FROM states LIMIT 310\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "states", "columns": ["headquartered_city", "access_count"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.sessions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fairtradecertifications\", conn)\ndf.to_sql(\"criminal_justice_reform_initiatives\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fairtradecertifications", "columns": null}], "writes": [{"table": "criminal_justice_reform_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO shared_escooters SELECT away_team_points, avg_speed FROM airportdata WHERE away_team_points > 186\"\n", "labels": {"reads": [{"table": "airportdata", "columns": ["away_team_points", "avg_speed"]}], "writes": [{"table": "shared_escooters", "columns": ["away_team_points", "avg_speed"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"party_services\")\nsrc.write.insertInto(\"restaurant\", overwrite=True)\n", "labels": {"reads": [{"table": "party_services", "columns": null}], "writes": [{"table": "restaurant", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"nailpolishsales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cybersecuritybudget\")\n", "labels": {"reads": [{"table": "nailpolishsales", "columns": null}], "writes": [{"table": "cybersecuritybudget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO driverstandings SELECT customer_phone, booking_date, max_speed, ethical_manufacturing FROM rental WHERE customer_phone > 466\"\n", "labels": {"reads": [{"table": "rental", "columns": ["customer_phone", "booking_date", "max_speed", "ethical_manufacturing"]}], "writes": [{"table": "driverstandings", "columns": ["customer_phone", "booking_date", "max_speed", "ethical_manufacturing"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO initiatives (school_type, teacher_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "initiatives", "columns": ["school_type", "teacher_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO euro_champs_track_field SELECT line_number, dorm_name, timestamp FROM artist_info WHERE line_number > 500;\nEOF\n", "labels": {"reads": [{"table": "artist_info", "columns": ["line_number", "dorm_name", "timestamp"]}], "writes": [{"table": "euro_champs_track_field", "columns": ["line_number", "dorm_name", "timestamp"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT treatment_type, socialimpactscore FROM dw.clicks_di LIMIT 316\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "dw.clicks_di", "columns": ["treatment_type", "socialimpactscore"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT purchase_date, attorneyid FROM state_budget LIMIT 333\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO tv_shows SELECT reservoir_name, exhibitioncountry FROM spacecrafts WHERE reservoir_name > 59\")\n", "labels": {"reads": [{"table": "state_budget", "columns": ["purchase_date", "attorneyid"]}, {"table": "spacecrafts", "columns": ["reservoir_name", "exhibitioncountry"]}], "writes": [{"table": "tv_shows", "columns": ["reservoir_name", "exhibitioncountry"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO endowment (founder, session_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "endowment", "columns": ["founder", "session_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO communityengagementmetrics (athlete_name, num_of_staff) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "communityengagementmetrics", "columns": ["athlete_name", "num_of_staff"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"match_season\")\nsrc.write.insertInto(\"voting_record\", overwrite=True)\n", "labels": {"reads": [{"table": "match_season", "columns": null}], "writes": [{"table": "voting_record", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT inclusive_housing_policy, label FROM riskassessments LIMIT 13\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO al_jazeera_data SELECT college_id, time_month FROM bi.bi_exposure_hourly WHERE college_id > 27\")\n", "labels": {"reads": [{"table": "riskassessments", "columns": ["inclusive_housing_policy", "label"]}, {"table": "bi.bi_exposure_hourly", "columns": ["college_id", "time_month"]}], "writes": [{"table": "al_jazeera_data", "columns": ["college_id", "time_month"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT hourid, opponent_id FROM legalaidrequests LIMIT 186\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": ["hourid", "opponent_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM traditionalarts\"\n", "labels": {"reads": [{"table": "traditionalarts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"production\").toPandas()\ndf[[\"health_equity_metric_2\", \"experience\"]].to_sql(\"assets_frameworks\", engine, index=False)\n", "labels": {"reads": [{"table": "production", "columns": null}], "writes": [{"table": "assets_frameworks", "columns": ["health_equity_metric_2", "experience"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthcare_budget\").toPandas()\ndf[[\"eventattendance\", \"dysprosium_prod\"]].to_sql(\"sustainability\", engine, index=False)\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": [{"table": "sustainability", "columns": ["eventattendance", "dysprosium_prod"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM cars\", conn)\ndf.to_sql(\"airlines\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "cars", "columns": null}], "writes": [{"table": "airlines", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT country_id, zip FROM ads.ads_cart_item_hourly LIMIT 287\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": ["country_id", "zip"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO council_tax SELECT a.publisher, b.other_account_details FROM disabilitysupportprograms a JOIN textile_sourcing b ON a.host_city = b.host_city\"\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": null}, {"table": "textile_sourcing", "columns": null}], "writes": [{"table": "council_tax", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"flight_safety\")\nsrc.write.insertInto(\"dws.dws_coupon_use_di\", overwrite=True)\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO aid_missions SELECT resource, contributionid, lipstick_id FROM wind_turbines WHERE resource > 261;\nEOF\n", "labels": {"reads": [{"table": "wind_turbines", "columns": ["resource", "contributionid", "lipstick_id"]}], "writes": [{"table": "aid_missions", "columns": ["resource", "contributionid", "lipstick_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"pipelines\")\nsink_to_store(df, \"ads_coupon_use_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "pipelines", "columns": null}], "writes": [{"table": "ads_coupon_use_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO performance_scores SELECT a.mine_type, b.dishid FROM bi.clicks_df a JOIN mart.mart_sessions_di b ON a.costid = b.costid\"\n", "labels": {"reads": [{"table": "bi.clicks_df", "columns": null}, {"table": "mart.mart_sessions_di", "columns": null}], "writes": [{"table": "performance_scores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO researchprojects SELECT materialname, shippeddate FROM transportation_union WHERE materialname > 316\"\n", "labels": {"reads": [{"table": "transportation_union", "columns": ["materialname", "shippeddate"]}], "writes": [{"table": "researchprojects", "columns": ["materialname", "shippeddate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.users\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"authors\")\n", "labels": {"reads": [{"table": "stg.users", "columns": null}], "writes": [{"table": "authors", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"expensive_space_missions\").toPandas()\ndf[[\"cruelty_free\", \"awayteamid\"]].to_sql(\"bookings\", engine, index=False)\n", "labels": {"reads": [{"table": "expensive_space_missions", "columns": null}], "writes": [{"table": "bookings", "columns": ["cruelty_free", "awayteamid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods.products_hourly\")\nsrc.write.insertInto(\"support_tickets\", overwrite=True)\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": null}], "writes": [{"table": "support_tickets", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ods.ods_risk_score_df SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lat, long FROM dw_member_point_full LIMIT 438\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO latam_schema.education_budget SELECT playerid, ppos, certification_id, head FROM festival_detail WHERE playerid > 41\")\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": ["lat", "long"]}, {"table": "festival_detail", "columns": ["playerid", "ppos", "certification_id", "head"]}], "writes": [{"table": "latam_schema.education_budget", "columns": ["playerid", "ppos", "certification_id", "head"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO military_personnel SELECT low_income_neighborhood, fieldid FROM urban_initiatives WHERE low_income_neighborhood > 1\"\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": ["low_income_neighborhood", "fieldid"]}], "writes": [{"table": "military_personnel", "columns": ["low_income_neighborhood", "fieldid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO engineer_skills SELECT bats, stat_type FROM doctors WHERE bats > 258\"\n", "labels": {"reads": [{"table": "doctors", "columns": ["bats", "stat_type"]}], "writes": [{"table": "engineer_skills", "columns": ["bats", "stat_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO worker_union SELECT style, sid, reservoir_name, siteid FROM paris_train WHERE style > 31\"\n", "labels": {"reads": [{"table": "paris_train", "columns": ["style", "sid", "reservoir_name", "siteid"]}], "writes": [{"table": "worker_union", "columns": ["style", "sid", "reservoir_name", "siteid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO habitat3 SELECT entrydate, catalog_entry_name, committee, emp_jobcode FROM supportservices WHERE entrydate > 444\"\n", "labels": {"reads": [{"table": "supportservices", "columns": ["entrydate", "catalog_entry_name", "committee", "emp_jobcode"]}], "writes": [{"table": "habitat3", "columns": ["entrydate", "catalog_entry_name", "committee", "emp_jobcode"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO music_database SELECT continent, threat FROM charging_stations WHERE continent > 42\"\n", "labels": {"reads": [{"table": "charging_stations", "columns": ["continent", "threat"]}], "writes": [{"table": "music_database", "columns": ["continent", "threat"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"useracct\").toPandas()\ndf[[\"missionid\", \"extraction_state\"]].to_sql(\"disaster_response\", engine, index=False)\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": [{"table": "disaster_response", "columns": ["missionid", "extraction_state"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO platformg SELECT player_api_id, episode_number FROM global_sales_2022 WHERE player_api_id > 314;\nEOF\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": ["player_api_id", "episode_number"]}], "writes": [{"table": "platformg", "columns": ["player_api_id", "episode_number"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT classtype, attack_id FROM lessons\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"exhibition_visits\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "lessons", "columns": ["classtype", "attack_id"]}], "writes": [{"table": "exhibition_visits", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rehab_centers\").toPandas()\ndf[[\"program_type\", \"business_size\"]].to_sql(\"sales_2\", engine, index=False)\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": [{"table": "sales_2", "columns": ["program_type", "business_size"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"shipmentinfo\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"habitats\")\n", "labels": {"reads": [{"table": "shipmentinfo", "columns": null}], "writes": [{"table": "habitats", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO canada_cosmetics_preferences SELECT water_type, no_of_loans, fda_approved FROM recovery_program WHERE water_type > 193\")\n", "labels": {"reads": [{"table": "recovery_program", "columns": ["water_type", "no_of_loans", "fda_approved"]}], "writes": [{"table": "canada_cosmetics_preferences", "columns": ["water_type", "no_of_loans", "fda_approved"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bank_info SELECT last_service, advisory_id, vesselname, evaluationid FROM purchase WHERE last_service > 338\"\n", "labels": {"reads": [{"table": "purchase", "columns": ["last_service", "advisory_id", "vesselname", "evaluationid"]}], "writes": [{"table": "bank_info", "columns": ["last_service", "advisory_id", "vesselname", "evaluationid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_mitigation_projects\").toPandas()\ndf[[\"num_songs\", \"consider_rate\"]].to_sql(\"communitydevelopment\", engine, index=False)\n", "labels": {"reads": [{"table": "climate_mitigation_projects", "columns": null}], "writes": [{"table": "communitydevelopment", "columns": ["num_songs", "consider_rate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ethicalaibudget\")\nsrc.write.insertInto(\"electricvehicleadoption\", overwrite=True)\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO healthydelights (passenger_count, fleet_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "healthydelights", "columns": ["passenger_count", "fleet_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO space_missions_2 SELECT num_stops, store_id, partid FROM uel_top10 WHERE num_stops > 273\"\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["num_stops", "store_id", "partid"]}], "writes": [{"table": "space_missions_2", "columns": ["num_stops", "store_id", "partid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT algorithm_name, initiativename FROM defense_projects_sales LIMIT 93\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO bike_station_info SELECT vehicleid, volunteer_year, number_of_hosts FROM fans WHERE vehicleid > 456\")\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": ["algorithm_name", "initiativename"]}, {"table": "fans", "columns": ["vehicleid", "volunteer_year", "number_of_hosts"]}], "writes": [{"table": "bike_station_info", "columns": ["vehicleid", "volunteer_year", "number_of_hosts"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model greenbuildings depends on rooms\ndbt build -s greenbuildings --vars '{\"src\":\"rooms\"}'\n", "labels": {"reads": [{"table": "rooms", "columns": null}], "writes": [{"table": "greenbuildings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO traffic_violations SELECT starting_year, quality_rank FROM ai_safety_incidents WHERE starting_year > 440\"\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": ["starting_year", "quality_rank"]}], "writes": [{"table": "traffic_violations", "columns": ["starting_year", "quality_rank"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_tourism_practices\").toPandas()\ndf[[\"activity_date\", \"ship_date\"]].to_sql(\"genetics.experiments\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": null}], "writes": [{"table": "genetics.experiments", "columns": ["activity_date", "ship_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO ethics_violations SELECT strategy, wrestler_id, unitsperweek FROM has_amenity WHERE strategy > 392\"\n", "labels": {"reads": [{"table": "has_amenity", "columns": ["strategy", "wrestler_id", "unitsperweek"]}], "writes": [{"table": "ethics_violations", "columns": ["strategy", "wrestler_id", "unitsperweek"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT char_cells, operation_name FROM wildlife LIMIT 292\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "wildlife", "columns": ["char_cells", "operation_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO public_participation SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO defensespending SELECT round_number, fname, production_volume FROM vessel_tracking WHERE round_number > 11\"\n", "labels": {"reads": [{"table": "vessel_tracking", "columns": ["round_number", "fname", "production_volume"]}], "writes": [{"table": "defensespending", "columns": ["round_number", "fname", "production_volume"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO equipment_maintenance SELECT * FROM legacy\ncur.execute(\"SELECT bill_id, matchdate FROM shark_biomass LIMIT 207\")\n", "labels": {"reads": [{"table": "shark_biomass", "columns": ["bill_id", "matchdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT advisory_id, merchandise_id FROM instructors LIMIT 64;\nINSERT INTO imagery_archive SELECT state, restaurant_name FROM wind_energy_projects WHERE state > 427;\nSQL\n", "labels": {"reads": [{"table": "instructors", "columns": ["advisory_id", "merchandise_id"]}, {"table": "wind_energy_projects", "columns": ["state", "restaurant_name"]}], "writes": [{"table": "imagery_archive", "columns": ["state", "restaurant_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cargo_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cargo_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO culturalcompetency SELECT journalist_id, state_id, stat_type FROM ads.sessions_hourly WHERE journalist_id > 167\"\n", "labels": {"reads": [{"table": "ads.sessions_hourly", "columns": ["journalist_id", "state_id", "stat_type"]}], "writes": [{"table": "culturalcompetency", "columns": ["journalist_id", "state_id", "stat_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agricultural_innovation SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurant_revenue\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artist_data\")\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": [{"table": "artist_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT heritage_site, missiontype FROM trafficviolations LIMIT 348\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO hydro_power SELECT date_to, participatedinesports, round_number, employmentdate FROM accessible_tech_categories WHERE date_to > 9\")\n", "labels": {"reads": [{"table": "trafficviolations", "columns": ["heritage_site", "missiontype"]}, {"table": "accessible_tech_categories", "columns": ["date_to", "participatedinesports", "round_number", "employmentdate"]}], "writes": [{"table": "hydro_power", "columns": ["date_to", "participatedinesports", "round_number", "employmentdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"legislation\")\nupsert_to_sink(df, \"labels\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "legislation", "columns": null}], "writes": [{"table": "labels", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fans\"\n", "labels": {"reads": [{"table": "fans", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM tech_for_social_good\"\n", "labels": {"reads": [{"table": "tech_for_social_good", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO gameplatforms SELECT * FROM legacy\ncur.execute(\"SELECT matchid, creationyear FROM fishcaught LIMIT 464\")\n", "labels": {"reads": [{"table": "fishcaught", "columns": ["matchid", "creationyear"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"recruiters\")\nwrite_to_output(df, \"stg.stg_events_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "recruiters", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO vehicles SELECT stockid, pollutant_type, training_id, home_games FROM renewable_projects WHERE stockid > 85\"\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["stockid", "pollutant_type", "training_id", "home_games"]}], "writes": [{"table": "vehicles", "columns": ["stockid", "pollutant_type", "training_id", "home_games"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO animal_budget SELECT ai_algorithm_id, propertyid FROM ads.sessions_hourly WHERE ai_algorithm_id > 400\")\n", "labels": {"reads": [{"table": "ads.sessions_hourly", "columns": ["ai_algorithm_id", "propertyid"]}], "writes": [{"table": "animal_budget", "columns": ["ai_algorithm_id", "propertyid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cyber_incidents SELECT * FROM legacy\ncur.execute(\"SELECT theatrename, is_false FROM vr_adopters LIMIT 259\")\n", "labels": {"reads": [{"table": "vr_adopters", "columns": ["theatrename", "is_false"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.ods_events_daily SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT artifactid, business_size FROM school_districts LIMIT 245\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "school_districts", "columns": ["artifactid", "business_size"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT outcome, cmi_details FROM miningwaterusage LIMIT 410\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": ["outcome", "cmi_details"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO socially_responsible_loans SELECT stop, fan_age, city_code, incident_count FROM singer_in_concert WHERE stop > 500\"\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["stop", "fan_age", "city_code", "incident_count"]}], "writes": [{"table": "socially_responsible_loans", "columns": ["stop", "fan_age", "city_code", "incident_count"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO satisfaction SELECT yearadded, service_type, rate, writer FROM bi.bi_payments_full WHERE yearadded > 249\"\n", "labels": {"reads": [{"table": "bi.bi_payments_full", "columns": ["yearadded", "service_type", "rate", "writer"]}], "writes": [{"table": "satisfaction", "columns": ["yearadded", "service_type", "rate", "writer"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table provider_training --target-dir /tmp/land\n", "labels": {"reads": [{"table": "provider_training", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO flight_safety SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_payments_delta SELECT clicks, text_of_notes, characteristic_type_code FROM convictions WHERE clicks > 12\"\n", "labels": {"reads": [{"table": "convictions", "columns": ["clicks", "text_of_notes", "characteristic_type_code"]}], "writes": [{"table": "ads.ads_payments_delta", "columns": ["clicks", "text_of_notes", "characteristic_type_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artsandcrafts\").toPandas()\ndf[[\"amount\", \"movie_id\"]].to_sql(\"sourcing\", engine, index=False)\n", "labels": {"reads": [{"table": "artsandcrafts", "columns": null}], "writes": [{"table": "sourcing", "columns": ["amount", "movie_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO recreation_centers SELECT investment_round, advocate_name, emp_dob, played FROM emissions WHERE investment_round > 459\"\n", "labels": {"reads": [{"table": "emissions", "columns": ["investment_round", "advocate_name", "emp_dob", "played"]}], "writes": [{"table": "recreation_centers", "columns": ["investment_round", "advocate_name", "emp_dob", "played"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO infrastructure_projects SELECT deliverydate, base_id, framework_id, quality_rank FROM defense_spending_3 WHERE deliverydate > 187\")\n", "labels": {"reads": [{"table": "defense_spending_3", "columns": ["deliverydate", "base_id", "framework_id", "quality_rank"]}], "writes": [{"table": "infrastructure_projects", "columns": ["deliverydate", "base_id", "framework_id", "quality_rank"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.coal_reserve_remaining > 481).all()\n# src table: affiliated_with\nengine.execute(\"INSERT INTO projectemployees SELECT * FROM affiliated_with\")\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": [{"table": "projectemployees", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"drug_approvals\")\nsrc.write.insertInto(\"tech_workers_union\", overwrite=True)\n", "labels": {"reads": [{"table": "drug_approvals", "columns": null}], "writes": [{"table": "tech_workers_union", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_research\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"criticalincidents\")\n", "labels": {"reads": [{"table": "marine_life_research", "columns": null}], "writes": [{"table": "criticalincidents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model co2_emissions depends on medicine_enzyme_interaction\ndbt run --select co2_emissions --vars '{\"src\":\"medicine_enzyme_interaction\"}'\n", "labels": {"reads": [{"table": "medicine_enzyme_interaction", "columns": null}], "writes": [{"table": "co2_emissions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO school SELECT a.individual_middle_name, b.fault_status FROM multimodalhubs a JOIN sfc_articles b ON a.sessionid = b.sessionid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": null}, {"table": "sfc_articles", "columns": null}], "writes": [{"table": "school", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO carbonoffsetinitiatives SELECT a.dock_status, b.overall_rating FROM mobile_usage a JOIN loan b ON a.individual_middle_name = b.individual_middle_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mobile_usage", "columns": null}, {"table": "loan", "columns": null}], "writes": [{"table": "carbonoffsetinitiatives", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"libraries\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dp_articles\")\n", "labels": {"reads": [{"table": "libraries", "columns": null}], "writes": [{"table": "dp_articles", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods.member_point_df (amenity_name, missiontype) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods.member_point_df", "columns": ["amenity_name", "missiontype"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"reverselogisticstransactions\")\npush_to_warehouse(df, \"state_info\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": null}], "writes": [{"table": "state_info", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT document_status_code, news_story_id FROM asset_parts\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"livestock\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "asset_parts", "columns": ["document_status_code", "news_story_id"]}], "writes": [{"table": "livestock", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table makeup_products --columns club_id,environmental_impact_score --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "makeup_products", "columns": ["club_id", "environmental_impact_score"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_shipments_df\").toPandas()\ndf[[\"fine_amount\", \"half\"]].to_sql(\"brands\", engine, index=False)\n", "labels": {"reads": [{"table": "ods_shipments_df", "columns": null}], "writes": [{"table": "brands", "columns": ["fine_amount", "half"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO electricvehicleadoption SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO caribbeansea SELECT * FROM legacy\ncur.execute(\"SELECT trip_id, treatment_name FROM onlineengagement LIMIT 282\")\n", "labels": {"reads": [{"table": "onlineengagement", "columns": ["trip_id", "treatment_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO harvest_permits (data_usage, date_of_notes) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "harvest_permits", "columns": ["data_usage", "date_of_notes"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"dws.dws_orders_full\")\nsave_to_sink(df, \"donationhistory\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": null}], "writes": [{"table": "donationhistory", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO traditional_arts SELECT room_count, album_id, post_id FROM coach WHERE room_count > 95\"\n", "labels": {"reads": [{"table": "coach", "columns": ["room_count", "album_id", "post_id"]}], "writes": [{"table": "traditional_arts", "columns": ["room_count", "album_id", "post_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO document_structures SELECT wage, algorithmic_fairness_score, vessel_name FROM prices WHERE wage > 205\"\n", "labels": {"reads": [{"table": "prices", "columns": ["wage", "algorithmic_fairness_score", "vessel_name"]}], "writes": [{"table": "document_structures", "columns": ["wage", "algorithmic_fairness_score", "vessel_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"drought_data\").toPandas()\ndf[[\"satellite\", \"ram_mib\"]].to_sql(\"high_risk\", engine, index=False)\n", "labels": {"reads": [{"table": "drought_data", "columns": null}], "writes": [{"table": "high_risk", "columns": ["satellite", "ram_mib"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model stories depends on military_aircraft_maintenance\ndbt build --select stories --vars '{\"src\":\"military_aircraft_maintenance\"}'\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": null}], "writes": [{"table": "stories", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods.shipments_df (num_stops, year_working) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods.shipments_df", "columns": ["num_stops", "year_working"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO campuses SELECT eco_friendly, farmid FROM bookings WHERE eco_friendly > 475\"\n", "labels": {"reads": [{"table": "bookings", "columns": ["eco_friendly", "farmid"]}], "writes": [{"table": "campuses", "columns": ["eco_friendly", "farmid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO voting_record SELECT city_name, num_solo_exhibitions, goal_id, amount_used FROM habitat_preservation WHERE city_name > 245\"\n", "labels": {"reads": [{"table": "habitat_preservation", "columns": ["city_name", "num_solo_exhibitions", "goal_id", "amount_used"]}], "writes": [{"table": "voting_record", "columns": ["city_name", "num_solo_exhibitions", "goal_id", "amount_used"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO engineer_skills SELECT detected_at, eliminated_by, horizontal_bar_points, mouse_id FROM collectivebargaining WHERE detected_at > 76\"], check=True)\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": ["detected_at", "eliminated_by", "horizontal_bar_points", "mouse_id"]}], "writes": [{"table": "engineer_skills", "columns": ["detected_at", "eliminated_by", "horizontal_bar_points", "mouse_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO obesity SELECT ticket_price, membership_type, agency, itemname FROM dwd.dwd_device_log_delta WHERE ticket_price > 179\"\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": ["ticket_price", "membership_type", "agency", "itemname"]}], "writes": [{"table": "obesity", "columns": ["ticket_price", "membership_type", "agency", "itemname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.coupon_use_daily SELECT a.inspection_id, b.accommodationtype FROM vr_tech a JOIN legalaidrequests b ON a.last_workout_date = b.last_workout_date\"\n", "labels": {"reads": [{"table": "vr_tech", "columns": null}, {"table": "legalaidrequests", "columns": null}], "writes": [{"table": "dwd.coupon_use_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM store_district\", conn)\ndf.to_sql(\"union_membership\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "store_district", "columns": null}], "writes": [{"table": "union_membership", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO category_revenue (grant_name, is_accessible) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "category_revenue", "columns": ["grant_name", "is_accessible"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO product_reviews SELECT a.crop, b.fish_count FROM equipmentsales a JOIN vendorfabrics b ON a.physician = b.physician\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "equipmentsales", "columns": null}, {"table": "vendorfabrics", "columns": null}], "writes": [{"table": "product_reviews", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.dws_member_point_di (formats, dataset) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_member_point_di", "columns": ["formats", "dataset"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workforce_development_programs\").toPandas()\ndf[[\"donator_name\", \"denomination\"]].to_sql(\"part_faults\", engine, index=False)\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": null}], "writes": [{"table": "part_faults", "columns": ["donator_name", "denomination"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO status (state_county, line_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "status", "columns": ["state_county", "line_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd.inventory_df\"\n", "labels": {"reads": [{"table": "dwd.inventory_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fundingdate > 220).all()\n# src table: atlantic_plate\nengine.execute(\"INSERT INTO agencies SELECT * FROM atlantic_plate\")\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": null}], "writes": [{"table": "agencies", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT matchid, min_dew_point_f FROM professionals\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"member\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "professionals", "columns": ["matchid", "min_dew_point_f"]}], "writes": [{"table": "member", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nresult = value * ratio + offset\nsql = \"INSERT INTO workforce_training SELECT a.visit_id, b.contributionid FROM city_labor_cost a JOIN security_incidents b ON a.max_cargo_weight = b.max_cargo_weight\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}, {"table": "security_incidents", "columns": null}], "writes": [{"table": "workforce_training", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO sustainable_projects SELECT sustainability_certified, ranking, eid, composer FROM marketingbudget WHERE sustainability_certified > 280\")\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["sustainability_certified", "ranking", "eid", "composer"]}], "writes": [{"table": "sustainable_projects", "columns": ["sustainability_certified", "ranking", "eid", "composer"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO rural_economy_2 SELECT outcome_code, pediatrician_id FROM fish_suppliers WHERE outcome_code > 380\"\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": ["outcome_code", "pediatrician_id"]}], "writes": [{"table": "rural_economy_2", "columns": ["outcome_code", "pediatrician_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\nsql = \"INSERT INTO rural_economy_2 SELECT a.siteid, b.shipmenttype FROM violations a JOIN ods.member_point_df b ON a.service = b.service\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "violations", "columns": null}, {"table": "ods.member_point_df", "columns": null}], "writes": [{"table": "rural_economy_2", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.campaigns_df\").toPandas()\ndf[[\"vendor_state\", \"doctorsper1000\"]].to_sql(\"mine\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": null}], "writes": [{"table": "mine", "columns": ["vendor_state", "doctorsper1000"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM climate_finance\", conn)\ndf.to_sql(\"candidates\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "climate_finance", "columns": null}], "writes": [{"table": "candidates", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.vendors_delta\"\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.clicks_hourly\").toPandas()\ndf[[\"tour_id\", \"bias_score\"]].to_sql(\"volunteers\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.clicks_hourly", "columns": null}], "writes": [{"table": "volunteers", "columns": ["tour_id", "bias_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT number_of_sightings, indigenous FROM innovation_metrics\", engine)\nimport logging\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"safetytests\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "innovation_metrics", "columns": ["number_of_sightings", "indigenous"]}], "writes": [{"table": "safetytests", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO galleryc SELECT plant, is_valid, order_date, strain_id FROM landfill_capacity_city_v2 WHERE plant > 317\"\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": ["plant", "is_valid", "order_date", "strain_id"]}], "writes": [{"table": "galleryc", "columns": ["plant", "is_valid", "order_date", "strain_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dispensaries SELECT build_date, vehicle, waste_amount FROM airportdata WHERE build_date > 14\")\n", "labels": {"reads": [{"table": "airportdata", "columns": ["build_date", "vehicle", "waste_amount"]}], "writes": [{"table": "dispensaries", "columns": ["build_date", "vehicle", "waste_amount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM brand_info\", conn)\ndf.to_sql(\"dw.dw_inventory_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "brand_info", "columns": null}], "writes": [{"table": "dw.dw_inventory_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"voyages\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "voyages", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO total_capacity SELECT number_of_sightings, monthlyactiveusers FROM artists_valuation WHERE number_of_sightings > 301\"\n", "labels": {"reads": [{"table": "artists_valuation", "columns": ["number_of_sightings", "monthlyactiveusers"]}], "writes": [{"table": "total_capacity", "columns": ["number_of_sightings", "monthlyactiveusers"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"manufacturers\").toPandas()\ndf[[\"matchid\", \"total_points\"]].to_sql(\"humanitarian_assistance\", engine, index=False)\n", "labels": {"reads": [{"table": "manufacturers", "columns": null}], "writes": [{"table": "humanitarian_assistance", "columns": ["matchid", "total_points"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"member_of_club\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"budgets\")\n", "labels": {"reads": [{"table": "member_of_club", "columns": null}], "writes": [{"table": "budgets", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT representative_name, program_category FROM vessel_types LIMIT 264\")\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO militarycyberops SELECT medicine_id, trip_id, billing_city, loadingend FROM donations2022 WHERE medicine_id > 29\")\n", "labels": {"reads": [{"table": "vessel_types", "columns": ["representative_name", "program_category"]}, {"table": "donations2022", "columns": ["medicine_id", "trip_id", "billing_city", "loadingend"]}], "writes": [{"table": "militarycyberops", "columns": ["medicine_id", "trip_id", "billing_city", "loadingend"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO efforts SELECT draft_details, veteran_unemployment_rate, co2_offset_amount FROM dw_users_full WHERE draft_details > 133\")\n", "labels": {"reads": [{"table": "dw_users_full", "columns": ["draft_details", "veteran_unemployment_rate", "co2_offset_amount"]}], "writes": [{"table": "efforts", "columns": ["draft_details", "veteran_unemployment_rate", "co2_offset_amount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO members (physical, porphyria) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "members", "columns": ["physical", "porphyria"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_coupon_use_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bridge\")\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": null}], "writes": [{"table": "bridge", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO innovation_grants SELECT snatch, share_in_percent, framework_name FROM renewable_energy_investments WHERE snatch > 406\"\n", "labels": {"reads": [{"table": "renewable_energy_investments", "columns": ["snatch", "share_in_percent", "framework_name"]}], "writes": [{"table": "innovation_grants", "columns": ["snatch", "share_in_percent", "framework_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO genetics_stats.research_projects SELECT report, partnership_id, tourist_id FROM dwd.dwd_vendors WHERE report > 463\"\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": ["report", "partnership_id", "tourist_id"]}], "writes": [{"table": "genetics_stats.research_projects", "columns": ["report", "partnership_id", "tourist_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO crime_stats SELECT document_structure_description, provider FROM inclusion_efforts WHERE document_structure_description > 185;\nEOF\n", "labels": {"reads": [{"table": "inclusion_efforts", "columns": ["document_structure_description", "provider"]}], "writes": [{"table": "crime_stats", "columns": ["document_structure_description", "provider"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM school_roster\"\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_users_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.mart_users_delta\")\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": [{"table": "mart.mart_users_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vehicles SELECT financial_capability_score, number_cities, address_id FROM student_mental_health WHERE financial_capability_score > 289\"], check=True)\n", "labels": {"reads": [{"table": "student_mental_health", "columns": ["financial_capability_score", "number_cities", "address_id"]}], "writes": [{"table": "vehicles", "columns": ["financial_capability_score", "number_cities", "address_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table daily_revenue --columns container_id,security_level --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "daily_revenue", "columns": ["container_id", "security_level"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT staff_details, transaction_amount FROM tokyo_water_consumption LIMIT 432;\nINSERT INTO stg.stg_events_hourly SELECT ad_type, instructor, culture FROM aid_missions WHERE ad_type > 212;\nSQL\n", "labels": {"reads": [{"table": "tokyo_water_consumption", "columns": ["staff_details", "transaction_amount"]}, {"table": "aid_missions", "columns": ["ad_type", "instructor", "culture"]}], "writes": [{"table": "stg.stg_events_hourly", "columns": ["ad_type", "instructor", "culture"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.decor > 304).all()\n# src table: tryout\nengine.execute(\"INSERT INTO event SELECT * FROM tryout\")\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": [{"table": "event", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"station_crime_rates\")\nsrc.write.insertInto(\"inspection\", overwrite=True)\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": null}], "writes": [{"table": "inspection", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO policyimpact SELECT customer_type_code, received_date, date_of_publication FROM aircraftsquadrons WHERE customer_type_code > 105\"\n", "labels": {"reads": [{"table": "aircraftsquadrons", "columns": ["customer_type_code", "received_date", "date_of_publication"]}], "writes": [{"table": "policyimpact", "columns": ["customer_type_code", "received_date", "date_of_publication"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hoursperweek, pollutant_type FROM submarine_canyons\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"urban_transportation\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": ["hoursperweek", "pollutant_type"]}], "writes": [{"table": "urban_transportation", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO disabilitysupportprograms SELECT * FROM legacy\ncur.execute(\"SELECT mappingid, restaurant_id FROM mart.device_log_hourly LIMIT 486\")\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": ["mappingid", "restaurant_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO vessel SELECT allergytype, market_id, uk_vat_number, asset_model FROM animal_budget WHERE allergytype > 51\"\n", "labels": {"reads": [{"table": "animal_budget", "columns": ["allergytype", "market_id", "uk_vat_number", "asset_model"]}], "writes": [{"table": "vessel", "columns": ["allergytype", "market_id", "uk_vat_number", "asset_model"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO manufacturersustainability SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO swimmer SELECT occupancy_rate, release_year FROM ods.ods_coupon_use_delta WHERE occupancy_rate > 236\"\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_delta", "columns": ["occupancy_rate", "release_year"]}], "writes": [{"table": "swimmer", "columns": ["occupancy_rate", "release_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT program_id, researcher_id FROM rural_projects LIMIT 453;\nINSERT INTO musicsales SELECT mine_location, content_type, visitor_count, date_of_latest_logon FROM workouts WHERE mine_location > 280;\nSQL\n", "labels": {"reads": [{"table": "rural_projects", "columns": ["program_id", "researcher_id"]}, {"table": "workouts", "columns": ["mine_location", "content_type", "visitor_count", "date_of_latest_logon"]}], "writes": [{"table": "musicsales", "columns": ["mine_location", "content_type", "visitor_count", "date_of_latest_logon"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"student_course_attendance\")\nsrc.write.insertInto(\"mart_exposure_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "student_course_attendance", "columns": null}], "writes": [{"table": "mart_exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"tourism_activities\")\nupsert_to_warehouse(df, \"ocean_basins\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tourism_activities", "columns": null}], "writes": [{"table": "ocean_basins", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.shipments_full SELECT * FROM legacy\ncur.execute(\"SELECT detention_type_code, awards FROM broadband_providers LIMIT 122\")\n", "labels": {"reads": [{"table": "broadband_providers", "columns": ["detention_type_code", "awards"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO investments SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO view_unit_status SELECT amount_used, clublocation FROM vessel_safety WHERE amount_used > 271\"], check=True)\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["amount_used", "clublocation"]}], "writes": [{"table": "view_unit_status", "columns": ["amount_used", "clublocation"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO green_projects SELECT application, asset_details FROM chemical_production_5 WHERE application > 168\"], check=True)\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": ["application", "asset_details"]}], "writes": [{"table": "green_projects", "columns": ["application", "asset_details"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stops SELECT * FROM legacy\ncur.execute(\"SELECT sessiondate, attendanceid FROM workout LIMIT 149\")\n", "labels": {"reads": [{"table": "workout", "columns": ["sessiondate", "attendanceid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fundingid, jan FROM chemicals_annual LIMIT 112\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": ["fundingid", "jan"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fleet SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"timber_sales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"defense_project_timelines\")\n", "labels": {"reads": [{"table": "timber_sales", "columns": null}], "writes": [{"table": "defense_project_timelines", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.institution > 137).all()\n# src table: crops_year\nengine.execute(\"INSERT INTO mobile_usage SELECT * FROM crops_year\")\n", "labels": {"reads": [{"table": "crops_year", "columns": null}], "writes": [{"table": "mobile_usage", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.num_attendees > 318).all()\n# src table: vessel_registry\nengine.execute(\"INSERT INTO bridges SELECT * FROM vessel_registry\")\n", "labels": {"reads": [{"table": "vessel_registry", "columns": null}], "writes": [{"table": "bridges", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural.bus_trips\").toPandas()\ndf[[\"stu_hrs\", \"country_name\"]].to_sql(\"defense_spending\", engine, index=False)\n", "labels": {"reads": [{"table": "rural.bus_trips", "columns": null}], "writes": [{"table": "defense_spending", "columns": ["stu_hrs", "country_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"classicgame\").toPandas()\ndf[[\"union_member_id\", \"destination_id\"]].to_sql(\"issues\", engine, index=False)\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": [{"table": "issues", "columns": ["union_member_id", "destination_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessel_positions SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT occupation, trip_end_time FROM ods.ods_risk_score_df\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"dws.payments_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": ["occupation", "trip_end_time"]}], "writes": [{"table": "dws.payments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"purchases\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"inst\")\n", "labels": {"reads": [{"table": "purchases", "columns": null}], "writes": [{"table": "inst", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM landfill_capacity\"\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM flu_shots\"\n", "labels": {"reads": [{"table": "flu_shots", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model displaced_people depends on festival_detail\ndbt build --select displaced_people --vars '{\"source_table\":\"festival_detail\"}'\n", "labels": {"reads": [{"table": "festival_detail", "columns": null}], "writes": [{"table": "displaced_people", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT total_investment, trips_on_day FROM dws_coupon_use\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"gender\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["total_investment", "trips_on_day"]}], "writes": [{"table": "gender", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT manufacturerid, lastname FROM fruitimport LIMIT 252\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "fruitimport", "columns": ["manufacturerid", "lastname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT researcher_id, negative FROM cargo_handling LIMIT 22;\nINSERT INTO marine_life_sightings SELECT component_type, rest_id, therapy_type FROM greenbuildings WHERE component_type > 67;\nSQL\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["researcher_id", "negative"]}, {"table": "greenbuildings", "columns": ["component_type", "rest_id", "therapy_type"]}], "writes": [{"table": "marine_life_sightings", "columns": ["component_type", "rest_id", "therapy_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO employeedata (founding_location, workforce_development) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "employeedata", "columns": ["founding_location", "workforce_development"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO marine_life_data SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.payments_di SELECT gas_fee, strategy_id, claim_type, excavation_site_id FROM school WHERE gas_fee > 499\"], check=True)\n", "labels": {"reads": [{"table": "school", "columns": ["gas_fee", "strategy_id", "claim_type", "excavation_site_id"]}], "writes": [{"table": "ads.payments_di", "columns": ["gas_fee", "strategy_id", "claim_type", "excavation_site_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO green_certification (stadium_id, product_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "green_certification", "columns": ["stadium_id", "product_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rental\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rental", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT college_location, analysis_date FROM labor_cost LIMIT 389\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bay_area_properties SELECT shipped_date, premise_id, skill_description FROM hospital WHERE shipped_date > 374\")\n", "labels": {"reads": [{"table": "labor_cost", "columns": ["college_location", "analysis_date"]}, {"table": "hospital", "columns": ["shipped_date", "premise_id", "skill_description"]}], "writes": [{"table": "bay_area_properties", "columns": ["shipped_date", "premise_id", "skill_description"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"sales_region\")\nsave_to_target(df, \"labor_hours\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "sales_region", "columns": null}], "writes": [{"table": "labor_hours", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO dws.dws_inventory_di SELECT a.participant, b.average_attendance FROM student_course_registrations a JOIN ocean_floor b ON a.arrival = b.arrival\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": null}, {"table": "ocean_floor", "columns": null}], "writes": [{"table": "dws.dws_inventory_di", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"textile_waste\")\npush_to_store(df, \"dw.dw_users_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "textile_waste", "columns": null}], "writes": [{"table": "dw.dw_users_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"footwear\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"atlantic_ocean\")\n", "labels": {"reads": [{"table": "footwear", "columns": null}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO gamesales SELECT a.zone, b.apid FROM ocean_salinity a JOIN dysprosiumproduction b ON a.apt_number = b.apt_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}, {"table": "dysprosiumproduction", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nsql = \"INSERT INTO astronaut_missions SELECT a.file_size, b.hours_billed FROM wastewater_treatment_plants a JOIN arrivals b ON a.competition_type = b.competition_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "wastewater_treatment_plants", "columns": null}, {"table": "arrivals", "columns": null}], "writes": [{"table": "astronaut_missions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fare_segments\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fashion_trend_data\")\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "fashion_trend_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ota_revenue SELECT pilot_id, feature_details, athlete FROM international_visitors WHERE pilot_id > 220\"\n", "labels": {"reads": [{"table": "international_visitors", "columns": ["pilot_id", "feature_details", "athlete"]}], "writes": [{"table": "ota_revenue", "columns": ["pilot_id", "feature_details", "athlete"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT customer_type_code, catalog_id FROM supplier_ethics LIMIT 51\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "supplier_ethics", "columns": ["customer_type_code", "catalog_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"associatedheritages\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"policyadvocacyevents\")\n", "labels": {"reads": [{"table": "associatedheritages", "columns": null}], "writes": [{"table": "policyadvocacyevents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM shared_ebikes\"\n", "labels": {"reads": [{"table": "shared_ebikes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO food_justice_contributors SELECT enable_third_party_ads, ironquantity, digital_channel, funder FROM dws_coupon_use WHERE enable_third_party_ads > 245\"\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["enable_third_party_ads", "ironquantity", "digital_channel", "funder"]}], "writes": [{"table": "food_justice_contributors", "columns": ["enable_third_party_ads", "ironquantity", "digital_channel", "funder"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO orders (client_first_name, primary_advisor) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "orders", "columns": ["client_first_name", "primary_advisor"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"user_reactions\")\nexport_to_output(df, \"obesity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "user_reactions", "columns": null}], "writes": [{"table": "obesity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model orders_daily depends on bi.payments_daily\ndbt build --models orders_daily --vars 'source: bi.payments_daily'\n", "labels": {"reads": [{"table": "bi.payments_daily", "columns": null}], "writes": [{"table": "orders_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical_processes\").toPandas()\ndf[[\"video_id\", \"plant_name\"]].to_sql(\"stg.stg_device_log_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": [{"table": "stg.stg_device_log_daily", "columns": ["video_id", "plant_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table art_pieces --target-dir /tmp/land\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dws.dws_events_hourly SELECT tank, contract_name, bank_id, commission_pct FROM fashion_trend_data WHERE tank > 30;\nEOF\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": ["tank", "contract_name", "bank_id", "commission_pct"]}], "writes": [{"table": "dws.dws_events_hourly", "columns": ["tank", "contract_name", "bank_id", "commission_pct"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO developers SELECT a.feature_details, b.drugname FROM mining_operation a JOIN underwater_trenches b ON a.birthday = b.birthday\"\n", "labels": {"reads": [{"table": "mining_operation", "columns": null}, {"table": "underwater_trenches", "columns": null}], "writes": [{"table": "developers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO construction_labor_stats SELECT flag, salinity, cmi_details FROM miningwaterusage WHERE flag > 52\"\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": ["flag", "salinity", "cmi_details"]}], "writes": [{"table": "construction_labor_stats", "columns": ["flag", "salinity", "cmi_details"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO e_scooter_trips SELECT a.start_date, b.drill_count FROM container_ships a JOIN dws.exposure_df b ON a.round_date = b.round_date\"\n", "labels": {"reads": [{"table": "container_ships", "columns": null}, {"table": "dws.exposure_df", "columns": null}], "writes": [{"table": "e_scooter_trips", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO cars SELECT injury_count, location_description, attendance_date FROM savings WHERE injury_count > 153\"\n", "labels": {"reads": [{"table": "savings", "columns": ["injury_count", "location_description", "attendance_date"]}], "writes": [{"table": "cars", "columns": ["injury_count", "location_description", "attendance_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT artifactname, domestic_passengers FROM city_tech\", engine)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"ads.ads_products_full\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "city_tech", "columns": ["artifactname", "domestic_passengers"]}], "writes": [{"table": "ads.ads_products_full", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw.dw_member_point_hourly SELECT * FROM legacy\ncur.execute(\"SELECT inventoryid, unitsperweek FROM team_members LIMIT 203\")\n", "labels": {"reads": [{"table": "team_members", "columns": ["inventoryid", "unitsperweek"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"baseball_teams\").toPandas()\ndf[[\"facility_name\", \"enzyme_id\"]].to_sql(\"forest\", engine, index=False)\n", "labels": {"reads": [{"table": "baseball_teams", "columns": null}], "writes": [{"table": "forest", "columns": ["facility_name", "enzyme_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO greenbuildings SELECT heritage_site, player, trench_name FROM supportprograms WHERE heritage_site > 427\"], check=True)\n", "labels": {"reads": [{"table": "supportprograms", "columns": ["heritage_site", "player", "trench_name"]}], "writes": [{"table": "greenbuildings", "columns": ["heritage_site", "player", "trench_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model staff_members depends on ship\ndbt build --models staff_members --vars '{\"source_table\":\"ship\"}'\n", "labels": {"reads": [{"table": "ship", "columns": null}], "writes": [{"table": "staff_members", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO shipment_data (menuitem, date_assigned_from) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "shipment_data", "columns": ["menuitem", "date_assigned_from"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.stadium_id > 235).all()\n# src table: useracct\nengine.execute(\"INSERT INTO charging_stations SELECT * FROM useracct\")\n", "labels": {"reads": [{"table": "useracct", "columns": null}], "writes": [{"table": "charging_stations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"public_transportation_sydney\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"economic_diversification\")\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO operation SELECT calendar_date, branch_id, ota_name, adults FROM customer_size_diversity WHERE calendar_date > 46\"\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["calendar_date", "branch_id", "ota_name", "adults"]}], "writes": [{"table": "operation", "columns": ["calendar_date", "branch_id", "ota_name", "adults"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM club\", conn)\ndf.to_sql(\"midwest_region\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "club", "columns": null}], "writes": [{"table": "midwest_region", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO legalaidrequests SELECT casestatus, hub_id FROM climate_mitigation_projects WHERE casestatus > 319\"], check=True)\n", "labels": {"reads": [{"table": "climate_mitigation_projects", "columns": ["casestatus", "hub_id"]}], "writes": [{"table": "legalaidrequests", "columns": ["casestatus", "hub_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT branch, initiative_region FROM iron_ore_production\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"storage_tech\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["branch", "initiative_region"]}], "writes": [{"table": "storage_tech", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO product_reviews SELECT salary, individual_middle_name, garment_id, prominence FROM waste_types WHERE salary > 9;\nEOF\n", "labels": {"reads": [{"table": "waste_types", "columns": ["salary", "individual_middle_name", "garment_id", "prominence"]}], "writes": [{"table": "product_reviews", "columns": ["salary", "individual_middle_name", "garment_id", "prominence"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.bi_sessions_daily SELECT violationid, doctor_id, devices FROM nz_tourism WHERE violationid > 234\"\n", "labels": {"reads": [{"table": "nz_tourism", "columns": ["violationid", "doctor_id", "devices"]}], "writes": [{"table": "bi.bi_sessions_daily", "columns": ["violationid", "doctor_id", "devices"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO circular_economy_companies SELECT closuredate, advocate_id, ocean_name, actual_delivery_date FROM galleryc WHERE closuredate > 59\"\n", "labels": {"reads": [{"table": "galleryc", "columns": ["closuredate", "advocate_id", "ocean_name", "actual_delivery_date"]}], "writes": [{"table": "circular_economy_companies", "columns": ["closuredate", "advocate_id", "ocean_name", "actual_delivery_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"container_receipts\")\nexport_to_store(df, \"accommodations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "container_receipts", "columns": null}], "writes": [{"table": "accommodations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table platform --columns sale_quantity,cruelty_free --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "platform", "columns": ["sale_quantity", "cruelty_free"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_hours\").toPandas()\ndf[[\"comments\", \"attorneyid\"]].to_sql(\"rent_arrears\", engine, index=False)\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": [{"table": "rent_arrears", "columns": ["comments", "attorneyid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ingredientsvegancrueltyfree SELECT transaction_amount, personnelbranch, matchid FROM hr.employees WHERE transaction_amount > 487;\nEOF\n", "labels": {"reads": [{"table": "hr.employees", "columns": ["transaction_amount", "personnelbranch", "matchid"]}], "writes": [{"table": "ingredientsvegancrueltyfree", "columns": ["transaction_amount", "personnelbranch", "matchid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vessel_incident_count SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT accessibility, species_id FROM dwd.vendors LIMIT 409;\nINSERT INTO legalaidrequests SELECT court_id, warehousename, material_type FROM rental WHERE court_id > 151;\nSQL\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": ["accessibility", "species_id"]}, {"table": "rental", "columns": ["court_id", "warehousename", "material_type"]}], "writes": [{"table": "legalaidrequests", "columns": ["court_id", "warehousename", "material_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"communication_scores\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"performingartsprograms\")\n", "labels": {"reads": [{"table": "communication_scores", "columns": null}], "writes": [{"table": "performingartsprograms", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO refugees SELECT promotionid, serve_id FROM ads.member_point WHERE promotionid > 232\"\n", "labels": {"reads": [{"table": "ads.member_point", "columns": ["promotionid", "serve_id"]}], "writes": [{"table": "refugees", "columns": ["promotionid", "serve_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO peacekeepingmissions SELECT score, budget_amount, report FROM trade_history WHERE score > 342\"\n", "labels": {"reads": [{"table": "trade_history", "columns": ["score", "budget_amount", "report"]}], "writes": [{"table": "peacekeepingmissions", "columns": ["score", "budget_amount", "report"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO tokyo_water_consumption SELECT value_points, salesperson FROM student_access WHERE value_points > 212\")\n", "labels": {"reads": [{"table": "student_access", "columns": ["value_points", "salesperson"]}], "writes": [{"table": "tokyo_water_consumption", "columns": ["value_points", "salesperson"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT transaction_amount, asset_disposed_date FROM supportprograms LIMIT 178;\nINSERT INTO artworks SELECT eco_friendly, contract_value, award FROM region_stats WHERE eco_friendly > 195;\nSQL\n", "labels": {"reads": [{"table": "supportprograms", "columns": ["transaction_amount", "asset_disposed_date"]}, {"table": "region_stats", "columns": ["eco_friendly", "contract_value", "award"]}], "writes": [{"table": "artworks", "columns": ["eco_friendly", "contract_value", "award"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO exhibitionsartworks SELECT quantitysold, outcome_code, stageposition, group_id FROM autonomousvehicles WHERE quantitysold > 57\"\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": ["quantitysold", "outcome_code", "stageposition", "group_id"]}], "writes": [{"table": "exhibitionsartworks", "columns": ["quantitysold", "outcome_code", "stageposition", "group_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"influencers\")\npersist_to_store(df, \"fans_merchandise_basketball\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "influencers", "columns": null}], "writes": [{"table": "fans_merchandise_basketball", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT attorney_id, cargo_weight FROM hospital_visits LIMIT 8;\nINSERT INTO dws.dws_inventory_di SELECT duration_ms, contract_type, founder_ethnicity, industry FROM mineral_extraction_us WHERE duration_ms > 199;\nSQL\n", "labels": {"reads": [{"table": "hospital_visits", "columns": ["attorney_id", "cargo_weight"]}, {"table": "mineral_extraction_us", "columns": ["duration_ms", "contract_type", "founder_ethnicity", "industry"]}], "writes": [{"table": "dws.dws_inventory_di", "columns": ["duration_ms", "contract_type", "founder_ethnicity", "industry"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.apt_type_code > 172).all()\n# src table: tours\nengine.execute(\"INSERT INTO defense_projects SELECT * FROM tours\")\n", "labels": {"reads": [{"table": "tours", "columns": null}], "writes": [{"table": "defense_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_device_log_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO containers SELECT * FROM legacy\ncur.execute(\"SELECT draft_details, borough FROM papers LIMIT 402\")\n", "labels": {"reads": [{"table": "papers", "columns": ["draft_details", "borough"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO livestock SELECT content_id, threats, recordid FROM marine_mammals WHERE content_id > 187\"\n", "labels": {"reads": [{"table": "marine_mammals", "columns": ["content_id", "threats", "recordid"]}], "writes": [{"table": "livestock", "columns": ["content_id", "threats", "recordid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO experts SELECT * FROM legacy\ncur.execute(\"SELECT attendanceid, wheels FROM mart.mart_products_hourly LIMIT 376\")\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": ["attendanceid", "wheels"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO renewableenergy SELECT employee_name, asset_id, school_code, call_date FROM ocean_depths WHERE employee_name > 246\"\n", "labels": {"reads": [{"table": "ocean_depths", "columns": ["employee_name", "asset_id", "school_code", "call_date"]}], "writes": [{"table": "renewableenergy", "columns": ["employee_name", "asset_id", "school_code", "call_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table plays_games --target-dir /tmp/land\n", "labels": {"reads": [{"table": "plays_games", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT creation, vote_percent FROM medical_facilities LIMIT 431\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO dws.dws_clicks_full SELECT flag, eventtype, pricepergram FROM department_stores WHERE flag > 369\")\n", "labels": {"reads": [{"table": "medical_facilities", "columns": ["creation", "vote_percent"]}, {"table": "department_stores", "columns": ["flag", "eventtype", "pricepergram"]}], "writes": [{"table": "dws.dws_clicks_full", "columns": ["flag", "eventtype", "pricepergram"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO salary SELECT num_of_stock, practiceid, contract_date FROM gameattendance WHERE num_of_stock > 40\"\n", "labels": {"reads": [{"table": "gameattendance", "columns": ["num_of_stock", "practiceid", "contract_date"]}], "writes": [{"table": "salary", "columns": ["num_of_stock", "practiceid", "contract_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO coralreefs SELECT farmid, doctorsper1000, hotel_chain_id, product_category_description FROM animal_species WHERE farmid > 129\"\n", "labels": {"reads": [{"table": "animal_species", "columns": ["farmid", "doctorsper1000", "hotel_chain_id", "product_category_description"]}], "writes": [{"table": "coralreefs", "columns": ["farmid", "doctorsper1000", "hotel_chain_id", "product_category_description"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT product, swimmer_id FROM public.forest_stats LIMIT 241;\nINSERT INTO inspections SELECT annual_entry_exit, excavation_site, artifacttype, farm_name FROM mental_health_professionals_2 WHERE annual_entry_exit > 73;\nSQL\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["product", "swimmer_id"]}, {"table": "mental_health_professionals_2", "columns": ["annual_entry_exit", "excavation_site", "artifacttype", "farm_name"]}], "writes": [{"table": "inspections", "columns": ["annual_entry_exit", "excavation_site", "artifacttype", "farm_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"clinics_sa\").toPandas()\ndf[[\"rental_rate\", \"dorm_name\"]].to_sql(\"permian_basin\", engine, index=False)\n", "labels": {"reads": [{"table": "clinics_sa", "columns": null}], "writes": [{"table": "permian_basin", "columns": ["rental_rate", "dorm_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"retail_workers_union\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"member_attendance\")\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": null}], "writes": [{"table": "member_attendance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO season_assists SELECT a.trainingid, b.researcher FROM exoplanet_discoveries a JOIN drivers b ON a.siteid = b.siteid\"\n", "labels": {"reads": [{"table": "exoplanet_discoveries", "columns": null}, {"table": "drivers", "columns": null}], "writes": [{"table": "season_assists", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel_incident_count SELECT grape, local FROM premises WHERE grape > 393\"\n", "labels": {"reads": [{"table": "premises", "columns": ["grape", "local"]}], "writes": [{"table": "vessel_incident_count", "columns": ["grape", "local"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd_events_delta SELECT grade, therapy_id, license_number FROM carbonoffsetinitiatives WHERE grade > 17\"\n", "labels": {"reads": [{"table": "carbonoffsetinitiatives", "columns": ["grade", "therapy_id", "license_number"]}], "writes": [{"table": "dwd_events_delta", "columns": ["grade", "therapy_id", "license_number"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sales_quarterly SELECT contract_count, plan_id FROM country_waste_generation WHERE contract_count > 103\"\n", "labels": {"reads": [{"table": "country_waste_generation", "columns": ["contract_count", "plan_id"]}], "writes": [{"table": "sales_quarterly", "columns": ["contract_count", "plan_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO brands SELECT a.province_name, b.length_feet FROM marine_life_research a JOIN chemicals_annual b ON a.lettergrade = b.lettergrade\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "marine_life_research", "columns": null}, {"table": "chemicals_annual", "columns": null}], "writes": [{"table": "brands", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO textile_sourcing SELECT amount_waste, patentexpirationdate, high_temperature FROM militaryequipmentsales WHERE amount_waste > 492\"\n", "labels": {"reads": [{"table": "militaryequipmentsales", "columns": ["amount_waste", "patentexpirationdate", "high_temperature"]}], "writes": [{"table": "textile_sourcing", "columns": ["amount_waste", "patentexpirationdate", "high_temperature"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO building_permits (primary_conference, grantid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "building_permits", "columns": ["primary_conference", "grantid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_payments_delta SELECT building_type, movie, destroyed_by_employee_id FROM europium_exports WHERE building_type > 414\"\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["building_type", "movie", "destroyed_by_employee_id"]}], "writes": [{"table": "ads.ads_payments_delta", "columns": ["building_type", "movie", "destroyed_by_employee_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"maintenance_requests\")\nsrc.write.insertInto(\"donorprograms\", overwrite=True)\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}], "writes": [{"table": "donorprograms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO companies SELECT facilityid, registration_date FROM concerts WHERE facilityid > 354\"\n", "labels": {"reads": [{"table": "concerts", "columns": ["facilityid", "registration_date"]}], "writes": [{"table": "companies", "columns": ["facilityid", "registration_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table intelligence_agency --columns sustainabilityrating,attorneyid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "intelligence_agency", "columns": ["sustainabilityrating", "attorneyid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO cargos SELECT dock_status, testtypeid, contactid, sale_amount FROM social_good_projects WHERE dock_status > 491\"\n", "labels": {"reads": [{"table": "social_good_projects", "columns": ["dock_status", "testtypeid", "contactid", "sale_amount"]}], "writes": [{"table": "cargos", "columns": ["dock_status", "testtypeid", "contactid", "sale_amount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO waterconservationinitiatives SELECT founder_identifies_as_lgbtq, dribbling FROM teacher_professional_development WHERE founder_identifies_as_lgbtq > 303;\nEOF\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": ["founder_identifies_as_lgbtq", "dribbling"]}], "writes": [{"table": "waterconservationinitiatives", "columns": ["founder_identifies_as_lgbtq", "dribbling"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climate_communication SELECT plant, waste_generation FROM financial_capability_program WHERE plant > 390\"\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": ["plant", "waste_generation"]}], "writes": [{"table": "climate_communication", "columns": ["plant", "waste_generation"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO shariah_compliant_loans SELECT gname, funder, violationtype FROM bi.bi_inventory_di WHERE gname > 368;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_inventory_di", "columns": ["gname", "funder", "violationtype"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["gname", "funder", "violationtype"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_payments_full\").toPandas()\ndf[[\"vrdevice\", \"plan_type\"]].to_sql(\"mentalhealthprofessional\", engine, index=False)\n", "labels": {"reads": [{"table": "ods.ods_payments_full", "columns": null}], "writes": [{"table": "mentalhealthprofessional", "columns": ["vrdevice", "plan_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT lastdonationdate, streamid FROM dws.dws_member_point_df\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"labour_productivity\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": ["lastdonationdate", "streamid"]}], "writes": [{"table": "labour_productivity", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO arrivals (day_of_week, client) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "arrivals", "columns": ["day_of_week", "client"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO highways (pollution_id, date_valid_from) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "highways", "columns": ["pollution_id", "date_valid_from"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"impact_asia\")\nsink_to_target(df, \"fish_biomass\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "impact_asia", "columns": null}], "writes": [{"table": "fish_biomass", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT contact_staff_id, practice_id FROM minor_in LIMIT 349;\nINSERT INTO customers_policies SELECT program_type, max_aperture FROM party WHERE program_type > 399;\nSQL\n", "labels": {"reads": [{"table": "minor_in", "columns": ["contact_staff_id", "practice_id"]}, {"table": "party", "columns": ["program_type", "max_aperture"]}], "writes": [{"table": "customers_policies", "columns": ["program_type", "max_aperture"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT shipmentid, framework FROM countries LIMIT 191\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO eventattendance SELECT camera_lens_id, mission_id, business_name, archeologist FROM cosmetic_sales WHERE camera_lens_id > 218\")\n", "labels": {"reads": [{"table": "countries", "columns": ["shipmentid", "framework"]}, {"table": "cosmetic_sales", "columns": ["camera_lens_id", "mission_id", "business_name", "archeologist"]}], "writes": [{"table": "eventattendance", "columns": ["camera_lens_id", "mission_id", "business_name", "archeologist"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO drug_approval SELECT a.supply_volume, b.lastname FROM sustainable_tourism_practices a JOIN fairtradecertification b ON a.playerid = b.playerid\"\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": null}, {"table": "fairtradecertification", "columns": null}], "writes": [{"table": "drug_approval", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tech_workers_union\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.stg_risk_score_df\")\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}], "writes": [{"table": "stg.stg_risk_score_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO neighborhoods SELECT architect_id, low_estimate, completion_date FROM spacecraft WHERE architect_id > 461\"], check=True)\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["architect_id", "low_estimate", "completion_date"]}], "writes": [{"table": "neighborhoods", "columns": ["architect_id", "low_estimate", "completion_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO support (num_shariah_compliant_investments, founding_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "support", "columns": ["num_shariah_compliant_investments", "founding_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO communityengagements SELECT a.unit_of_measure, b.budget_million FROM urban_farms a JOIN bi.bi_risk_score_full b ON a.retailer = b.retailer\"\n", "labels": {"reads": [{"table": "urban_farms", "columns": null}, {"table": "bi.bi_risk_score_full", "columns": null}], "writes": [{"table": "communityengagements", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"affordablehousing\")\nsrc.write.insertInto(\"waterconsumptionbyoperation\", overwrite=True)\n", "labels": {"reads": [{"table": "affordablehousing", "columns": null}], "writes": [{"table": "waterconsumptionbyoperation", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM exhibitionsartworks\", conn)\ndf.to_sql(\"transport\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": [{"table": "transport", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO view_product_availability SELECT * FROM legacy\ncur.execute(\"SELECT destination_id, mentalhealthscore FROM donors_region LIMIT 319\")\n", "labels": {"reads": [{"table": "donors_region", "columns": ["destination_id", "mentalhealthscore"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO restaurant SELECT a.trip_city, b.acidity_level FROM deliveryaddresses a JOIN stg.stg_campaigns b ON a.publication_id = b.publication_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "deliveryaddresses", "columns": null}, {"table": "stg.stg_campaigns", "columns": null}], "writes": [{"table": "restaurant", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mining_operation\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"media_library\")\n", "labels": {"reads": [{"table": "mining_operation", "columns": null}], "writes": [{"table": "media_library", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO irrigation_systems SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO attorney_billing SELECT orgid, destination_id, session_name FROM bikerental WHERE orgid > 332\"\n", "labels": {"reads": [{"table": "bikerental", "columns": ["orgid", "destination_id", "session_name"]}], "writes": [{"table": "attorney_billing", "columns": ["orgid", "destination_id", "session_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ota_revenue SELECT * FROM legacy\ncur.execute(\"SELECT tot_cred, production_volume FROM tracks LIMIT 70\")\n", "labels": {"reads": [{"table": "tracks", "columns": ["tot_cred", "production_volume"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artist\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"healthcare_centers\")\n", "labels": {"reads": [{"table": "artist", "columns": null}], "writes": [{"table": "healthcare_centers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO obesity SELECT last_maintenance_date, sustainability_rating, valuation FROM fairtradecertification WHERE last_maintenance_date > 447\"], check=True)\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": ["last_maintenance_date", "sustainability_rating", "valuation"]}], "writes": [{"table": "obesity", "columns": ["last_maintenance_date", "sustainability_rating", "valuation"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT startup_id, outcome_date FROM customer_transactions LIMIT 426\")\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO criminal_cases SELECT major, oppose_rate, anomaly FROM dw.dw_orders_hourly WHERE major > 12\")\n", "labels": {"reads": [{"table": "customer_transactions", "columns": ["startup_id", "outcome_date"]}, {"table": "dw.dw_orders_hourly", "columns": ["major", "oppose_rate", "anomaly"]}], "writes": [{"table": "criminal_cases", "columns": ["major", "oppose_rate", "anomaly"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table resource_extraction --target-dir /tmp/land\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM indie_artists\"\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO volunteers SELECT priceid, membership_id, booked_count FROM volunteer_registration WHERE priceid > 171\"], check=True)\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": ["priceid", "membership_id", "booked_count"]}], "writes": [{"table": "volunteers", "columns": ["priceid", "membership_id", "booked_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table workouts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "workouts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT budget_amount, circularsupplychain FROM mart_payments_df LIMIT 69\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": ["budget_amount", "circularsupplychain"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO highways SELECT * FROM legacy\ncur.execute(\"SELECT address_line_2, num_of_staff FROM culturalcompetencytrainings LIMIT 242\")\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": ["address_line_2", "num_of_staff"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO advisor SELECT * FROM legacy\ncur.execute(\"SELECT emp_dob, unitsperweek FROM procedures LIMIT 488\")\n", "labels": {"reads": [{"table": "procedures", "columns": ["emp_dob", "unitsperweek"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"deep_sea_species\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "deep_sea_species", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT business_size, sustainabilityrating FROM mining.company LIMIT 294\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO player SELECT district, num_students, tournament_id FROM agroecology_practices WHERE district > 245\")\n", "labels": {"reads": [{"table": "mining.company", "columns": ["business_size", "sustainabilityrating"]}, {"table": "agroecology_practices", "columns": ["district", "num_students", "tournament_id"]}], "writes": [{"table": "player", "columns": ["district", "num_students", "tournament_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sales_2 (host_city_id, sector_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sales_2", "columns": ["host_city_id", "sector_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO genre_songs SELECT issues, leader_name, ssn, carbon_offset_tons FROM testtypes WHERE issues > 10\"\n", "labels": {"reads": [{"table": "testtypes", "columns": ["issues", "leader_name", "ssn", "carbon_offset_tons"]}], "writes": [{"table": "genre_songs", "columns": ["issues", "leader_name", "ssn", "carbon_offset_tons"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.client > 241).all()\n# src table: school_roster\nengine.execute(\"INSERT INTO landfill_capacity_city_v2 SELECT * FROM school_roster\")\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": [{"table": "landfill_capacity_city_v2", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO humanitarian_assistance SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_reviews\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO mart_events_full SELECT a.donor_name, b.end_station_id FROM trainers a JOIN climate_finance_re b ON a.country_code = b.country_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trainers", "columns": null}, {"table": "climate_finance_re", "columns": null}], "writes": [{"table": "mart_events_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO journal SELECT industry_4_0, lot_id FROM faculty_participates_in WHERE industry_4_0 > 73\"\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": ["industry_4_0", "lot_id"]}], "writes": [{"table": "journal", "columns": ["industry_4_0", "lot_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM view_unit_status\", conn)\ndf.to_sql(\"factory_connections\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "view_unit_status", "columns": null}], "writes": [{"table": "factory_connections", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO restorative_justice_programs SELECT union_id, sessiondate, title, waste_type FROM oceanography WHERE union_id > 54\"\n", "labels": {"reads": [{"table": "oceanography", "columns": ["union_id", "sessiondate", "title", "waste_type"]}], "writes": [{"table": "restorative_justice_programs", "columns": ["union_id", "sessiondate", "title", "waste_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"criminal_justice_reform_initiatives\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"packages\")\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": null}], "writes": [{"table": "packages", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"seeds\")\nsrc.write.insertInto(\"mart.mart_device_log_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "seeds", "columns": null}], "writes": [{"table": "mart.mart_device_log_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO studies SELECT fund_id, release_date FROM station_crime_rates WHERE fund_id > 428\"\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": ["fund_id", "release_date"]}], "writes": [{"table": "studies", "columns": ["fund_id", "release_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO customer_payments SELECT price_in_dollar, price FROM carbon_prices_3 WHERE price_in_dollar > 105\"\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["price_in_dollar", "price"]}], "writes": [{"table": "customer_payments", "columns": ["price_in_dollar", "price"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO operations SELECT stories, threat, price_per_gram FROM mart.campaigns_full WHERE stories > 354\"], check=True)\n", "labels": {"reads": [{"table": "mart.campaigns_full", "columns": ["stories", "threat", "price_per_gram"]}], "writes": [{"table": "operations", "columns": ["stories", "threat", "price_per_gram"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table cultivators --target-dir /tmp/land\n", "labels": {"reads": [{"table": "cultivators", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.assigned_to_staff_id > 396).all()\n# src table: vessel_capacity\nengine.execute(\"INSERT INTO recycling_rates_oceania SELECT * FROM vessel_capacity\")\n", "labels": {"reads": [{"table": "vessel_capacity", "columns": null}], "writes": [{"table": "recycling_rates_oceania", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.mart_payments_df SELECT campaign, software_platform FROM ads.ads_refunds_hourly WHERE campaign > 107\"\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": ["campaign", "software_platform"]}], "writes": [{"table": "mart.mart_payments_df", "columns": ["campaign", "software_platform"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.stg_clicks_delta\")\nsrc.write.insertInto(\"loan\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.stg_clicks_delta", "columns": null}], "writes": [{"table": "loan", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mine\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mine", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"checking\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"caribbean_tourists\")\n", "labels": {"reads": [{"table": "checking", "columns": null}], "writes": [{"table": "caribbean_tourists", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_aircraft_maintenance\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO renewables.renewable_projects (amount, orderdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "renewables.renewable_projects", "columns": ["amount", "orderdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT catalog_level_name, workoutid FROM user_stats\", engine)\nimport logging\ndf.to_sql(\"farm_competition\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "user_stats", "columns": ["catalog_level_name", "workoutid"]}], "writes": [{"table": "farm_competition", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT family_name, cargo_id FROM biosensor.patents LIMIT 259\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO genre_songs SELECT effective_date, graphics_mode, length_feet, pieces FROM carbon_offset_initiatives WHERE effective_date > 453\")\n", "labels": {"reads": [{"table": "biosensor.patents", "columns": ["family_name", "cargo_id"]}, {"table": "carbon_offset_initiatives", "columns": ["effective_date", "graphics_mode", "length_feet", "pieces"]}], "writes": [{"table": "genre_songs", "columns": ["effective_date", "graphics_mode", "length_feet", "pieces"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT train_number, operationid FROM dw.member_point_daily LIMIT 72\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "dw.member_point_daily", "columns": ["train_number", "operationid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT marketing_region_descriptrion, preferred_foot FROM donorgender LIMIT 27\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "donorgender", "columns": ["marketing_region_descriptrion", "preferred_foot"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shariahfinance depends on regional_archaeologists\ndbt build --models shariahfinance --vars '{\"src\":\"regional_archaeologists\"}'\n", "labels": {"reads": [{"table": "regional_archaeologists", "columns": null}], "writes": [{"table": "shariahfinance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO environmentalimpact SELECT address, province FROM birds WHERE address > 410\"], check=True)\n", "labels": {"reads": [{"table": "birds", "columns": ["address", "province"]}], "writes": [{"table": "environmentalimpact", "columns": ["address", "province"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.dws_refunds_daily (vesselid, garment_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_refunds_daily", "columns": ["vesselid", "garment_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.shipments_daily (kids, community_members) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.shipments_daily", "columns": ["kids", "community_members"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"culturalcompetencytrainings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.mart_device_log SELECT member_name, sales, supportrepid, postal_code FROM russia_nato_diplomacy WHERE member_name > 117\"\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": ["member_name", "sales", "supportrepid", "postal_code"]}], "writes": [{"table": "mart.mart_device_log", "columns": ["member_name", "sales", "supportrepid", "postal_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM inventory\", conn)\ndf.to_sql(\"mart.mart_events_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "inventory", "columns": null}], "writes": [{"table": "mart.mart_events_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gamegenres SELECT num_employees, lastdonationdate, mission_date FROM coowners WHERE num_employees > 446\"\n", "labels": {"reads": [{"table": "coowners", "columns": ["num_employees", "lastdonationdate", "mission_date"]}], "writes": [{"table": "gamegenres", "columns": ["num_employees", "lastdonationdate", "mission_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"models_safety\")\ndump_to_output(df, \"civilcases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "models_safety", "columns": null}], "writes": [{"table": "civilcases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fare_segments\", conn)\ndf.to_sql(\"pacific_ocean\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "pacific_ocean", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO skincareproducts SELECT a.speed, b.max_salary FROM traffic_citations a JOIN fundings b ON a.shelter_id = b.shelter_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "traffic_citations", "columns": null}, {"table": "fundings", "columns": null}], "writes": [{"table": "skincareproducts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table marketingbudget --target-dir /tmp/land\n", "labels": {"reads": [{"table": "marketingbudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT primaryaffiliation, venue_id FROM dw_payments LIMIT 360;\nINSERT INTO policyadvocacyevents SELECT investmenttype, hoursspent, teamname, appointment_duration FROM satellitematerials WHERE investmenttype > 179;\nSQL\n", "labels": {"reads": [{"table": "dw_payments", "columns": ["primaryaffiliation", "venue_id"]}, {"table": "satellitematerials", "columns": ["investmenttype", "hoursspent", "teamname", "appointment_duration"]}], "writes": [{"table": "policyadvocacyevents", "columns": ["investmenttype", "hoursspent", "teamname", "appointment_duration"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO vr_adopters SELECT * FROM legacy\ncur.execute(\"SELECT product_stock_number, acidification_level FROM organisations LIMIT 337\")\n", "labels": {"reads": [{"table": "organisations", "columns": ["product_stock_number", "acidification_level"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model player_f depends on students\ndbt build --select player_f --vars '{\"src\":\"students\"}'\n", "labels": {"reads": [{"table": "students", "columns": null}], "writes": [{"table": "player_f", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"public_transportation_routes\")\nsrc.write.insertInto(\"waterconsumptionbyoperation\", overwrite=True)\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": null}], "writes": [{"table": "waterconsumptionbyoperation", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.production_bopd > 226).all()\n# src table: evidence_based_policies\nengine.execute(\"INSERT INTO nba SELECT * FROM evidence_based_policies\")\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": null}], "writes": [{"table": "nba", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO agriculturalinvestments SELECT a.student_details, b.province_name FROM government_transparency a JOIN agri_innov b ON a.airport = b.airport\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "government_transparency", "columns": null}, {"table": "agri_innov", "columns": null}], "writes": [{"table": "agriculturalinvestments", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO drama_workshop_groups SELECT case_number, emission_date FROM militaryinnovations WHERE case_number > 58\"\n", "labels": {"reads": [{"table": "militaryinnovations", "columns": ["case_number", "emission_date"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["case_number", "emission_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO storage SELECT equipment_type, performance_id, tournament_name FROM refugee_support WHERE equipment_type > 73\"\n", "labels": {"reads": [{"table": "refugee_support", "columns": ["equipment_type", "performance_id", "tournament_name"]}], "writes": [{"table": "storage", "columns": ["equipment_type", "performance_id", "tournament_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT claim_status_description, contact_staff_id FROM user_stats\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"supplier_addresses\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "user_stats", "columns": ["claim_status_description", "contact_staff_id"]}], "writes": [{"table": "supplier_addresses", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO industrial_building_energy_efficiency SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM educators\", conn)\ndf.to_sql(\"sustainableprojects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "educators", "columns": null}], "writes": [{"table": "sustainableprojects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table stg.stg_events_di --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO italy_culture SELECT usage, saledate, treatment_year FROM weekly_weather WHERE usage > 348\"], check=True)\n", "labels": {"reads": [{"table": "weekly_weather", "columns": ["usage", "saledate", "treatment_year"]}], "writes": [{"table": "italy_culture", "columns": ["usage", "saledate", "treatment_year"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dockingdate, hiv FROM community_programs LIMIT 106\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "community_programs", "columns": ["dockingdate", "hiv"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table user_activity --target-dir /tmp/land\n", "labels": {"reads": [{"table": "user_activity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO donorprograms SELECT inspectionscore, attendance_id, destination FROM ethicalaibudget WHERE inspectionscore > 255;\nEOF\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": ["inspectionscore", "attendance_id", "destination"]}], "writes": [{"table": "donorprograms", "columns": ["inspectionscore", "attendance_id", "destination"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO budgets SELECT funding_source, lesson_id, wins, scientist FROM bi.risk_score_df WHERE funding_source > 123\")\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": ["funding_source", "lesson_id", "wins", "scientist"]}], "writes": [{"table": "budgets", "columns": ["funding_source", "lesson_id", "wins", "scientist"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_coupon_use_di\")\nsrc.write.insertInto(\"bioprocess.engineering_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": null}], "writes": [{"table": "bioprocess.engineering_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"postseason\")\nsrc.write.insertInto(\"factory_water\", overwrite=True)\n", "labels": {"reads": [{"table": "postseason", "columns": null}], "writes": [{"table": "factory_water", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT vice_president_vote, investor FROM ytterbium_supply LIMIT 355;\nINSERT INTO teacher_development_race SELECT code, fault_short_name, drug_name, movie FROM chemicalproducts WHERE code > 315;\nSQL\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": ["vice_president_vote", "investor"]}, {"table": "chemicalproducts", "columns": ["code", "fault_short_name", "drug_name", "movie"]}], "writes": [{"table": "teacher_development_race", "columns": ["code", "fault_short_name", "drug_name", "movie"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO public_transportation_routes SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM weights\"\n", "labels": {"reads": [{"table": "weights", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO concert_events SELECT * FROM legacy\ncur.execute(\"SELECT grantamount, restypedescription FROM solar_farms LIMIT 176\")\n", "labels": {"reads": [{"table": "solar_farms", "columns": ["grantamount", "restypedescription"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"traditional_arts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"donationsbycause\")\n", "labels": {"reads": [{"table": "traditional_arts", "columns": null}], "writes": [{"table": "donationsbycause", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM gamegenres\"\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM policyholders\", conn)\ndf.to_sql(\"dws.inventory_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "policyholders", "columns": null}], "writes": [{"table": "dws.inventory_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"unionmembers\")\nsrc.write.insertInto(\"products_in_events\", overwrite=True)\n", "labels": {"reads": [{"table": "unionmembers", "columns": null}], "writes": [{"table": "products_in_events", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT handling_date, shelter_name FROM companies LIMIT 218;\nINSERT INTO ods.ods_member_point_df SELECT spacecraft_model, machinery_id, timestamp, creationyear FROM dws.dws_refunds_hourly WHERE spacecraft_model > 150;\nSQL\n", "labels": {"reads": [{"table": "companies", "columns": ["handling_date", "shelter_name"]}, {"table": "dws.dws_refunds_hourly", "columns": ["spacecraft_model", "machinery_id", "timestamp", "creationyear"]}], "writes": [{"table": "ods.ods_member_point_df", "columns": ["spacecraft_model", "machinery_id", "timestamp", "creationyear"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM wastedata\"\n", "labels": {"reads": [{"table": "wastedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mental_health_center SELECT is_cruelty_free, mediatorid, average_age, service_id FROM athletes_performance WHERE is_cruelty_free > 499;\nEOF\n", "labels": {"reads": [{"table": "athletes_performance", "columns": ["is_cruelty_free", "mediatorid", "average_age", "service_id"]}], "writes": [{"table": "mental_health_center", "columns": ["is_cruelty_free", "mediatorid", "average_age", "service_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wellbeing_program_participants SELECT * FROM legacy\ncur.execute(\"SELECT concertid, therapy_type FROM procedures LIMIT 317\")\n", "labels": {"reads": [{"table": "procedures", "columns": ["concertid", "therapy_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO climate_finance_re SELECT injury_date, strain, is_valid, users_engaged FROM bi.bi_risk_score_df WHERE injury_date > 95;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_risk_score_df", "columns": ["injury_date", "strain", "is_valid", "users_engaged"]}], "writes": [{"table": "climate_finance_re", "columns": ["injury_date", "strain", "is_valid", "users_engaged"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO producers (restaurant_name, num_schools) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "producers", "columns": ["restaurant_name", "num_schools"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO sponsorship_donations (inspectiondate, workoutname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "sponsorship_donations", "columns": ["inspectiondate", "workoutname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO military_aircraft_maintenance SELECT vehicleid, other_item_details, poll_source, grant_start_date FROM smartcitycosts WHERE vehicleid > 142\"], check=True)\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": ["vehicleid", "other_item_details", "poll_source", "grant_start_date"]}], "writes": [{"table": "military_aircraft_maintenance", "columns": ["vehicleid", "other_item_details", "poll_source", "grant_start_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO manager_award SELECT reviews, accelerator_id FROM surveylocations WHERE reviews > 287\")\n", "labels": {"reads": [{"table": "surveylocations", "columns": ["reviews", "accelerator_id"]}], "writes": [{"table": "manager_award", "columns": ["reviews", "accelerator_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi.bi_campaigns_daily (q1_2022_views, fueldate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["q1_2022_views", "fueldate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM chemicalbatches\"\n", "labels": {"reads": [{"table": "chemicalbatches", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.grant_amount > 283).all()\n# src table: african_union_countries\nengine.execute(\"INSERT INTO bus_routes SELECT * FROM african_union_countries\")\n", "labels": {"reads": [{"table": "african_union_countries", "columns": null}], "writes": [{"table": "bus_routes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"intelligence_agents\")\nsink_to_store(df, \"student_course_attendance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}], "writes": [{"table": "student_course_attendance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO features SELECT college, permit_number FROM wastewater_treatment_plants WHERE college > 172;\nEOF\n", "labels": {"reads": [{"table": "wastewater_treatment_plants", "columns": ["college", "permit_number"]}], "writes": [{"table": "features", "columns": ["college", "permit_number"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO satellite_deployment SELECT a.meeting_count, b.duration_ms FROM player_sessions a JOIN category_revenue b ON a.monthly_rental = b.monthly_rental\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "player_sessions", "columns": null}, {"table": "category_revenue", "columns": null}], "writes": [{"table": "satellite_deployment", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO schoolc SELECT floor_area_m2, country_of_origin FROM maintenance_contracts WHERE floor_area_m2 > 361\"\n", "labels": {"reads": [{"table": "maintenance_contracts", "columns": ["floor_area_m2", "country_of_origin"]}], "writes": [{"table": "schoolc", "columns": ["floor_area_m2", "country_of_origin"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO genetics.projects SELECT a.porphyria, b.restaurant_name FROM virtual_tourism a JOIN ytterbium_supply b ON a.mailing_date = b.mailing_date\"\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": null}, {"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "genetics.projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM heart_rate_data\", conn)\ndf.to_sql(\"emerging_markets.digital_assets\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": null}], "writes": [{"table": "emerging_markets.digital_assets", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stat_type, post_category FROM fare_collection\", engine)\nimport logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"hires\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["stat_type", "post_category"]}], "writes": [{"table": "hires", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO wildlife_sanctuaries (issues, round_number) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "wildlife_sanctuaries", "columns": ["issues", "round_number"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO club SELECT ai_id, courtname, route_short_name FROM ticketspending WHERE ai_id > 50\"\n", "labels": {"reads": [{"table": "ticketspending", "columns": ["ai_id", "courtname", "route_short_name"]}], "writes": [{"table": "club", "columns": ["ai_id", "courtname", "route_short_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model class depends on singer_in_concert\ndbt build -s class --vars 'source: singer_in_concert'\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "class", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT fundingamount, participation_id FROM field_production LIMIT 332\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dws.shipments_daily SELECT recipe_id, transportation_method FROM ods.clicks_full WHERE recipe_id > 210\")\n", "labels": {"reads": [{"table": "field_production", "columns": ["fundingamount", "participation_id"]}, {"table": "ods.clicks_full", "columns": ["recipe_id", "transportation_method"]}], "writes": [{"table": "dws.shipments_daily", "columns": ["recipe_id", "transportation_method"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table chemical_composition --columns issued_date,element --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "chemical_composition", "columns": ["issued_date", "element"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"subjects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"industry_funding\")\n", "labels": {"reads": [{"table": "subjects", "columns": null}], "writes": [{"table": "industry_funding", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO packages SELECT contractor_name, support_id, energy_efficiency_kwh_m2_year FROM fans_merchandise_basketball WHERE contractor_name > 184\"\n", "labels": {"reads": [{"table": "fans_merchandise_basketball", "columns": ["contractor_name", "support_id", "energy_efficiency_kwh_m2_year"]}], "writes": [{"table": "packages", "columns": ["contractor_name", "support_id", "energy_efficiency_kwh_m2_year"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT spill_name, vulnerability_score FROM workouts LIMIT 38\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO season_assists SELECT apid, round_date, games, rental_date FROM labor_productivity WHERE apid > 330\")\n", "labels": {"reads": [{"table": "workouts", "columns": ["spill_name", "vulnerability_score"]}, {"table": "labor_productivity", "columns": ["apid", "round_date", "games", "rental_date"]}], "writes": [{"table": "season_assists", "columns": ["apid", "round_date", "games", "rental_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO crops SELECT a.awayteamid, b.session_name FROM continent a JOIN music_events b ON a.investor_id = b.investor_id\"\n", "labels": {"reads": [{"table": "continent", "columns": null}, {"table": "music_events", "columns": null}], "writes": [{"table": "crops", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tickets SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM latam_schema.education_budget\", conn)\ndf.to_sql(\"defenseprojects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": null}], "writes": [{"table": "defenseprojects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT checkin, injury_date FROM maintenance LIMIT 398;\nINSERT INTO singer_in_concert SELECT emission_date, membername, days FROM research.species WHERE emission_date > 98;\nSQL\n", "labels": {"reads": [{"table": "maintenance", "columns": ["checkin", "injury_date"]}, {"table": "research.species", "columns": ["emission_date", "membername", "days"]}], "writes": [{"table": "singer_in_concert", "columns": ["emission_date", "membername", "days"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model economic_diversification_efforts depends on culturalpractices\ndbt run --models economic_diversification_efforts --vars '{\"source_table\":\"culturalpractices\"}'\n", "labels": {"reads": [{"table": "culturalpractices", "columns": null}], "writes": [{"table": "economic_diversification_efforts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO item_prices SELECT a.screen_mode, b.assessmentid FROM ingredientsvegancrueltyfree a JOIN dw.dw_inventory_df b ON a.wage_increase = b.wage_increase\"\n", "labels": {"reads": [{"table": "ingredientsvegancrueltyfree", "columns": null}, {"table": "dw.dw_inventory_df", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO timber_production SELECT * FROM legacy\ncur.execute(\"SELECT spacecraft_id, incident_type FROM ads.ads_exposure_di LIMIT 272\")\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["spacecraft_id", "incident_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO mining_operation_data SELECT incident_name, sales_amount, festival_name, precedent_id FROM mart.shipments_delta WHERE incident_name > 485\"\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": ["incident_name", "sales_amount", "festival_name", "precedent_id"]}], "writes": [{"table": "mining_operation_data", "columns": ["incident_name", "sales_amount", "festival_name", "precedent_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"seeds\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"food_justice\")\n", "labels": {"reads": [{"table": "seeds", "columns": null}], "writes": [{"table": "food_justice", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM excavation_sites\", conn)\ndf.to_sql(\"authorship\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "excavation_sites", "columns": null}], "writes": [{"table": "authorship", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model platformstats depends on tech_accessibility_funding\ndbt run --select platformstats --vars '{\"source_table\":\"tech_accessibility_funding\"}'\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": null}], "writes": [{"table": "platformstats", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO seafood SELECT * FROM legacy\ncur.execute(\"SELECT num_shariah_compliant_investments, log_entry_date FROM cybersecurity_strategies LIMIT 412\")\n", "labels": {"reads": [{"table": "cybersecurity_strategies", "columns": ["num_shariah_compliant_investments", "log_entry_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hotel_name, case_number FROM company_info LIMIT 305;\nINSERT INTO cars SELECT ad_id, prof_num, screen_mode, fine FROM packages WHERE ad_id > 104;\nSQL\n", "labels": {"reads": [{"table": "company_info", "columns": ["hotel_name", "case_number"]}, {"table": "packages", "columns": ["ad_id", "prof_num", "screen_mode", "fine"]}], "writes": [{"table": "cars", "columns": ["ad_id", "prof_num", "screen_mode", "fine"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.archaeologist_id > 204).all()\n# src table: ads.ads_cart_item_hourly\nengine.execute(\"INSERT INTO contributions SELECT * FROM ads.ads_cart_item_hourly\")\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": null}], "writes": [{"table": "contributions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO world_heritage_sites SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"submarine_canyons\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"harvest_permits\")\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": null}], "writes": [{"table": "harvest_permits", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO circularsupplychain SELECT county_name, end_station_id, balance FROM mart.mart_risk_score_hourly WHERE county_name > 378\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_risk_score_hourly", "columns": ["county_name", "end_station_id", "balance"]}], "writes": [{"table": "circularsupplychain", "columns": ["county_name", "end_station_id", "balance"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO visitordemographics SELECT savingsid, stationid, threat_type FROM party_forms WHERE savingsid > 313\"\n", "labels": {"reads": [{"table": "party_forms", "columns": ["savingsid", "stationid", "threat_type"]}], "writes": [{"table": "visitordemographics", "columns": ["savingsid", "stationid", "threat_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM indie_artists\"\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT s_id, time_second FROM catalog_contents\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"dw.dw_member_point_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "catalog_contents", "columns": ["s_id", "time_second"]}], "writes": [{"table": "dw.dw_member_point_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM pacific_ocean\", conn)\ndf.to_sql(\"agriculturalinvestments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": null}], "writes": [{"table": "agriculturalinvestments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO chemical_concentration (subject, cargo_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "chemical_concentration", "columns": ["subject", "cargo_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT complaint_id, town_city FROM smart_contracts LIMIT 483\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO characteristics SELECT fish_count, ll_hours FROM police_officers_tx WHERE fish_count > 394\")\n", "labels": {"reads": [{"table": "smart_contracts", "columns": ["complaint_id", "town_city"]}, {"table": "police_officers_tx", "columns": ["fish_count", "ll_hours"]}], "writes": [{"table": "characteristics", "columns": ["fish_count", "ll_hours"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO historicalcontexts (production_value, songname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "historicalcontexts", "columns": ["production_value", "songname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.inventory_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"field\")\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": null}], "writes": [{"table": "field", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO esports_teams SELECT approach, donorage FROM bi.bi_orders_hourly WHERE approach > 336\"\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": ["approach", "donorage"]}], "writes": [{"table": "esports_teams", "columns": ["approach", "donorage"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"footwear\")\ndump_to_sink(df, \"minor_in\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "footwear", "columns": null}], "writes": [{"table": "minor_in", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO track SELECT num_shariah_compliant_investments, bioreactor_id, mediatypeid, mh_id FROM enrolled_in WHERE num_shariah_compliant_investments > 174\"\n", "labels": {"reads": [{"table": "enrolled_in", "columns": ["num_shariah_compliant_investments", "bioreactor_id", "mediatypeid", "mh_id"]}], "writes": [{"table": "track", "columns": ["num_shariah_compliant_investments", "bioreactor_id", "mediatypeid", "mh_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO defense_project_timelines SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"policyanalysis\")\ndump_to_store(df, \"counties\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "policyanalysis", "columns": null}], "writes": [{"table": "counties", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO travel_advisory SELECT claim_status_description, venue, trade FROM nursing_homes WHERE claim_status_description > 449;\nEOF\n", "labels": {"reads": [{"table": "nursing_homes", "columns": ["claim_status_description", "venue", "trade"]}], "writes": [{"table": "travel_advisory", "columns": ["claim_status_description", "venue", "trade"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table provider_training --target-dir /tmp/land\n", "labels": {"reads": [{"table": "provider_training", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT maintenance_contract_company_id, site_id FROM singer_in_concert LIMIT 74;\nINSERT INTO stg.stg_users_di SELECT sd_id, date_incident_end, policy_area, marketing_region_descriptrion FROM trainingprograms WHERE sd_id > 256;\nSQL\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["maintenance_contract_company_id", "site_id"]}, {"table": "trainingprograms", "columns": ["sd_id", "date_incident_end", "policy_area", "marketing_region_descriptrion"]}], "writes": [{"table": "stg.stg_users_di", "columns": ["sd_id", "date_incident_end", "policy_area", "marketing_region_descriptrion"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.inventory_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bay_area_properties\")\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": [{"table": "bay_area_properties", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO spacemissions SELECT course_name, vendor_name FROM clothingsales WHERE course_name > 147\"\n", "labels": {"reads": [{"table": "clothingsales", "columns": ["course_name", "vendor_name"]}], "writes": [{"table": "spacemissions", "columns": ["course_name", "vendor_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT event_id, athlete FROM tb_reports LIMIT 285\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO militarybases SELECT typical_selling_price, station_name FROM subway WHERE typical_selling_price > 474\")\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["event_id", "athlete"]}, {"table": "subway", "columns": ["typical_selling_price", "station_name"]}], "writes": [{"table": "militarybases", "columns": ["typical_selling_price", "station_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"talent_acquisition\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"autonomousvehicleaccidents\")\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": null}], "writes": [{"table": "autonomousvehicleaccidents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO concentrateprices SELECT employee, labor_practice FROM project_timeline WHERE employee > 97;\nEOF\n", "labels": {"reads": [{"table": "project_timeline", "columns": ["employee", "labor_practice"]}], "writes": [{"table": "concentrateprices", "columns": ["employee", "labor_practice"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vehicle (painting_name, hotel_chain_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vehicle", "columns": ["painting_name", "hotel_chain_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"flu_shots\")\nexport_to_output(df, \"fair_trade_brands\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "flu_shots", "columns": null}], "writes": [{"table": "fair_trade_brands", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.shipments_df SELECT team_id_winner, date_payment_made, directed_by FROM disabilityadvocacy WHERE team_id_winner > 43\"\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": ["team_id_winner", "date_payment_made", "directed_by"]}], "writes": [{"table": "mart.shipments_df", "columns": ["team_id_winner", "date_payment_made", "directed_by"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.mart_users_di\", conn)\ndf.to_sql(\"legal_technology_funding\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": null}], "writes": [{"table": "legal_technology_funding", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO artsheritage SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_reviews\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.refunds\")\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": null}], "writes": [{"table": "stg.refunds", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO dwd.products_hourly SELECT a.lettergrade, b.application FROM dws_shipments_df a JOIN stg.sessions_full b ON a.fish_count = b.fish_count\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dws_shipments_df", "columns": null}, {"table": "stg.sessions_full", "columns": null}], "writes": [{"table": "dwd.products_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecuritybudget\").toPandas()\ndf[[\"product_description\", \"complaint_status_code\"]].to_sql(\"instructors\", engine, index=False)\n", "labels": {"reads": [{"table": "cybersecuritybudget", "columns": null}], "writes": [{"table": "instructors", "columns": ["product_description", "complaint_status_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO cerium_production SELECT fleet_id, amountdonated, avg_depth, club_name FROM textileworkers WHERE fleet_id > 83\")\n", "labels": {"reads": [{"table": "textileworkers", "columns": ["fleet_id", "amountdonated", "avg_depth", "club_name"]}], "writes": [{"table": "cerium_production", "columns": ["fleet_id", "amountdonated", "avg_depth", "club_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM organic_products\", conn)\ndf.to_sql(\"carbon_emissions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "organic_products", "columns": null}], "writes": [{"table": "carbon_emissions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO virtual_tour_revenue SELECT * FROM legacy\ncur.execute(\"SELECT nid, ai_model FROM menu_item LIMIT 287\")\n", "labels": {"reads": [{"table": "menu_item", "columns": ["nid", "ai_model"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.stg_products_delta (transactionid, pets_allowed_yn) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_products_delta", "columns": ["transactionid", "pets_allowed_yn"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO daily_oil_production (primary_conference, log_entry_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "daily_oil_production", "columns": ["primary_conference", "log_entry_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO community_members SELECT vegan, parameters FROM country_landfill_capacity WHERE vegan > 29\"], check=True)\n", "labels": {"reads": [{"table": "country_landfill_capacity", "columns": ["vegan", "parameters"]}], "writes": [{"table": "community_members", "columns": ["vegan", "parameters"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dws.cart_item_full SELECT employee_name, trend_id, maintenance_date, network FROM org_comms WHERE employee_name > 460\")\n", "labels": {"reads": [{"table": "org_comms", "columns": ["employee_name", "trend_id", "maintenance_date", "network"]}], "writes": [{"table": "dws.cart_item_full", "columns": ["employee_name", "trend_id", "maintenance_date", "network"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_payments_hourly SELECT a.genre_is, b.carrierid FROM tvshows a JOIN carbonoffsetinitiatives b ON a.water_type = b.water_type\"\n", "labels": {"reads": [{"table": "tvshows", "columns": null}, {"table": "carbonoffsetinitiatives", "columns": null}], "writes": [{"table": "mart.mart_payments_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.next_entry_id > 210).all()\n# src table: document_structures\nengine.execute(\"INSERT INTO dwd_sessions_hourly SELECT * FROM document_structures\")\n", "labels": {"reads": [{"table": "document_structures", "columns": null}], "writes": [{"table": "dwd_sessions_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cosmetic_sales\").toPandas()\ndf[[\"element_id\", \"restypedescription\"]].to_sql(\"airlines\", engine, index=False)\n", "labels": {"reads": [{"table": "cosmetic_sales", "columns": null}], "writes": [{"table": "airlines", "columns": ["element_id", "restypedescription"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_diplomacy\").toPandas()\ndf[[\"working_year_starts\", \"digital_channel\"]].to_sql(\"item_inventory\", engine, index=False)\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": null}], "writes": [{"table": "item_inventory", "columns": ["working_year_starts", "digital_channel"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table producesupplier --target-dir /tmp/land\n", "labels": {"reads": [{"table": "producesupplier", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM container\"\n", "labels": {"reads": [{"table": "container", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM nyc_subway\"\n", "labels": {"reads": [{"table": "nyc_subway", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"france_culture\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tourist_attraction_features\")\n", "labels": {"reads": [{"table": "france_culture", "columns": null}], "writes": [{"table": "tourist_attraction_features", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_buildings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"satellite_missions_large\")\n", "labels": {"reads": [{"table": "green_buildings", "columns": null}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table travel_advisory --target-dir /tmp/land\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tracklists (lot_details, typeid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tracklists", "columns": ["lot_details", "typeid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM flu_cases\", conn)\ndf.to_sql(\"dw.dw_users_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "flu_cases", "columns": null}], "writes": [{"table": "dw.dw_users_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model water_distribution depends on mars_rovers\ndbt build --models water_distribution --vars '{\"src\":\"mars_rovers\"}'\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": [{"table": "water_distribution", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM tennis_players\"\n", "labels": {"reads": [{"table": "tennis_players", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO authors SELECT virtual_tour_engagement_time, suburb FROM wind_projects WHERE virtual_tour_engagement_time > 254\"\n", "labels": {"reads": [{"table": "wind_projects", "columns": ["virtual_tour_engagement_time", "suburb"]}], "writes": [{"table": "authors", "columns": ["virtual_tour_engagement_time", "suburb"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ref_hotel_star_ratings depends on design_standards\ndbt build --models ref_hotel_star_ratings --vars '{\"src\":\"design_standards\"}'\n", "labels": {"reads": [{"table": "design_standards", "columns": null}], "writes": [{"table": "ref_hotel_star_ratings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO maintenance_schedule SELECT custid, workoutdate FROM rural_development.agriculture_projects WHERE custid > 184\"\n", "labels": {"reads": [{"table": "rural_development.agriculture_projects", "columns": ["custid", "workoutdate"]}], "writes": [{"table": "maintenance_schedule", "columns": ["custid", "workoutdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.mart_refunds_di SELECT roaming_country, professional_development_programs, num_transactions, theatrename FROM galleries WHERE roaming_country > 411\"\n", "labels": {"reads": [{"table": "galleries", "columns": ["roaming_country", "professional_development_programs", "num_transactions", "theatrename"]}], "writes": [{"table": "mart.mart_refunds_di", "columns": ["roaming_country", "professional_development_programs", "num_transactions", "theatrename"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO miningwaterusage SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO casesbyyear SELECT a.crop, b.primaryaffiliation FROM stg.stg_events_di a JOIN bi.bi_payments b ON a.contractorid = b.contractorid\"\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}, {"table": "bi.bi_payments", "columns": null}], "writes": [{"table": "casesbyyear", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO industrial_customers SELECT a.mean_sea_level_pressure_inches, b.menuitemid FROM donationhistory a JOIN uniteddefense.equipmentsales b ON a.contractorid = b.contractorid\"\n", "labels": {"reads": [{"table": "donationhistory", "columns": null}, {"table": "uniteddefense.equipmentsales", "columns": null}], "writes": [{"table": "industrial_customers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads_exposure_hourly (materialname, is_organic) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads_exposure_hourly", "columns": ["materialname", "is_organic"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.incident_date > 360).all()\n# src table: mine\nengine.execute(\"INSERT INTO labor_statistics SELECT * FROM mine\")\n", "labels": {"reads": [{"table": "mine", "columns": null}], "writes": [{"table": "labor_statistics", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO food_items SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO emergencyservices SELECT porphyria, app_id, sales_id, number_city_affected FROM port WHERE porphyria > 6\"\n", "labels": {"reads": [{"table": "port", "columns": ["porphyria", "app_id", "sales_id", "number_city_affected"]}], "writes": [{"table": "emergencyservices", "columns": ["porphyria", "app_id", "sales_id", "number_city_affected"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO carbon_prices SELECT grant_end_date, cultural_diversity, high_temperature, account_type FROM legislation WHERE grant_end_date > 497;\nEOF\n", "labels": {"reads": [{"table": "legislation", "columns": ["grant_end_date", "cultural_diversity", "high_temperature", "account_type"]}], "writes": [{"table": "carbon_prices", "columns": ["grant_end_date", "cultural_diversity", "high_temperature", "account_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stops\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stops", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_refunds_hourly SELECT a.hometown, b.scientist FROM southeast_providers a JOIN field5 b ON a.dorm_name = b.dorm_name\"\n", "labels": {"reads": [{"table": "southeast_providers", "columns": null}, {"table": "field5", "columns": null}], "writes": [{"table": "mart.mart_refunds_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ref_incident_type SELECT assets_billion, num_cases FROM latam_schema.education_budget WHERE assets_billion > 74\"], check=True)\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": ["assets_billion", "num_cases"]}], "writes": [{"table": "ref_incident_type", "columns": ["assets_billion", "num_cases"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT claim_date, prod_id FROM timber_sales LIMIT 47\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "timber_sales", "columns": ["claim_date", "prod_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO humanitarianmissions SELECT * FROM legacy\ncur.execute(\"SELECT license_type, secretary_vote FROM animal_budget LIMIT 440\")\n", "labels": {"reads": [{"table": "animal_budget", "columns": ["license_type", "secretary_vote"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dws.dws_coupon_use_hourly --columns contributor,sector_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_coupon_use_hourly", "columns": ["contributor", "sector_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO policyholders SELECT roomid, pieces FROM projectemployees WHERE roomid > 142\")\n", "labels": {"reads": [{"table": "projectemployees", "columns": ["roomid", "pieces"]}], "writes": [{"table": "policyholders", "columns": ["roomid", "pieces"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO specieswatertemp SELECT a.devices, b.portfolio_id FROM education a JOIN arrivals b ON a.detention_summary = b.detention_summary\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "education", "columns": null}, {"table": "arrivals", "columns": null}], "writes": [{"table": "specieswatertemp", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO socialimpactinvestments SELECT individual_id, age_group FROM membership_data WHERE individual_id > 453\")\n", "labels": {"reads": [{"table": "membership_data", "columns": ["individual_id", "age_group"]}], "writes": [{"table": "socialimpactinvestments", "columns": ["individual_id", "age_group"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO revenue SELECT a.shale_play, b.audienceid FROM parking_fines a JOIN crime_incidents b ON a.sport_id = b.sport_id\"\n", "labels": {"reads": [{"table": "parking_fines", "columns": null}, {"table": "crime_incidents", "columns": null}], "writes": [{"table": "revenue", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT product_stock_number, ad_id FROM menu LIMIT 4\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO exam_results SELECT cloud_cover, production_quantity, movie FROM tree_habitat_associations WHERE cloud_cover > 140\")\n", "labels": {"reads": [{"table": "menu", "columns": ["product_stock_number", "ad_id"]}, {"table": "tree_habitat_associations", "columns": ["cloud_cover", "production_quantity", "movie"]}], "writes": [{"table": "exam_results", "columns": ["cloud_cover", "production_quantity", "movie"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT recruitername, crs_code FROM sustainability_fact LIMIT 332\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO dates SELECT budgeted, contract_value FROM dw.dw_inventory_delta WHERE budgeted > 125\")\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": ["recruitername", "crs_code"]}, {"table": "dw.dw_inventory_delta", "columns": ["budgeted", "contract_value"]}], "writes": [{"table": "dates", "columns": ["budgeted", "contract_value"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dysprosiumproduction SELECT wellname, eventid, gold, nationality FROM roads WHERE wellname > 84;\nEOF\n", "labels": {"reads": [{"table": "roads", "columns": ["wellname", "eventid", "gold", "nationality"]}], "writes": [{"table": "dysprosiumproduction", "columns": ["wellname", "eventid", "gold", "nationality"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model deep_sea_expeditions depends on grant\ndbt run -s deep_sea_expeditions --vars 'source: grant'\n", "labels": {"reads": [{"table": "grant", "columns": null}], "writes": [{"table": "deep_sea_expeditions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO militaryequipment SELECT a.development_type, b.case_outcome FROM rural_resources a JOIN investor_activities b ON a.other_details = b.other_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rural_resources", "columns": null}, {"table": "investor_activities", "columns": null}], "writes": [{"table": "militaryequipment", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT gradepoint, city_area FROM mars_missions\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"textile_suppliers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mars_missions", "columns": ["gradepoint", "city_area"]}], "writes": [{"table": "textile_suppliers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT emission_date, dish_id FROM teacher_pd LIMIT 7\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "teacher_pd", "columns": ["emission_date", "dish_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO marine_mammals SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO student_tests_taken SELECT supplier_country, service FROM fields_production WHERE supplier_country > 341\"], check=True)\n", "labels": {"reads": [{"table": "fields_production", "columns": ["supplier_country", "service"]}], "writes": [{"table": "student_tests_taken", "columns": ["supplier_country", "service"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO trends_2022 (successful_cb, seal_species) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "trends_2022", "columns": ["successful_cb", "seal_species"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO albums SELECT * FROM legacy\ncur.execute(\"SELECT experienceid, strain FROM wine LIMIT 339\")\n", "labels": {"reads": [{"table": "wine", "columns": ["experienceid", "strain"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lender_name, technician_id FROM emergency_categories LIMIT 498\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO us_military_personnel SELECT salary, item_sold, habitat_id, applicant FROM evsales WHERE salary > 165\")\n", "labels": {"reads": [{"table": "emergency_categories", "columns": ["lender_name", "technician_id"]}, {"table": "evsales", "columns": ["salary", "item_sold", "habitat_id", "applicant"]}], "writes": [{"table": "us_military_personnel", "columns": ["salary", "item_sold", "habitat_id", "applicant"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT raceid, center_id FROM playerscores\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"genetics.projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "playerscores", "columns": ["raceid", "center_id"]}], "writes": [{"table": "genetics.projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"shipments\")\nsrc.write.insertInto(\"community_development_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "shipments", "columns": null}], "writes": [{"table": "community_development_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO project_timeline (total_horses, kids) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "project_timeline", "columns": ["total_horses", "kids"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO arctic_sightings SELECT governor, length_feet, country_code, stayid FROM dw_member_point_full WHERE governor > 81\"\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": ["governor", "length_feet", "country_code", "stayid"]}], "writes": [{"table": "arctic_sightings", "columns": ["governor", "length_feet", "country_code", "stayid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO waste SELECT make, total_horses, tree_species FROM dysprosiumproduction WHERE make > 417\"], check=True)\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": ["make", "total_horses", "tree_species"]}], "writes": [{"table": "waste", "columns": ["make", "total_horses", "tree_species"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table bi.bi_inventory_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO engineer_skills SELECT cultural_competency_score, book_title FROM equipmentsales WHERE cultural_competency_score > 331\"\n", "labels": {"reads": [{"table": "equipmentsales", "columns": ["cultural_competency_score", "book_title"]}], "writes": [{"table": "engineer_skills", "columns": ["cultural_competency_score", "book_title"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg_users_daily SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT starttime, jobtitle FROM student_lifelong_learning LIMIT 242\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "student_lifelong_learning", "columns": ["starttime", "jobtitle"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO budgets SELECT park, accident_date FROM feed WHERE park > 446;\nEOF\n", "labels": {"reads": [{"table": "feed", "columns": ["park", "accident_date"]}], "writes": [{"table": "budgets", "columns": ["park", "accident_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO species_forests SELECT a.policy_number, b.trip_id FROM ads.ads_refunds_hourly a JOIN storage_projects b ON a.campaign_name = b.campaign_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.ads_refunds_hourly", "columns": null}, {"table": "storage_projects", "columns": null}], "writes": [{"table": "species_forests", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table buildingpermits --target-dir /tmp/land\n", "labels": {"reads": [{"table": "buildingpermits", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"mart.mart_coupon_use_full\")\nupsert_to_store(df, \"biomes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_full", "columns": null}], "writes": [{"table": "biomes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT order_details, acc_regular_season FROM cybersecuritybudget LIMIT 334;\nINSERT INTO climber SELECT labor_id, cell_mobile_number, coowner_name FROM dwd_coupon_use_hourly WHERE labor_id > 71;\nSQL\n", "labels": {"reads": [{"table": "cybersecuritybudget", "columns": ["order_details", "acc_regular_season"]}, {"table": "dwd_coupon_use_hourly", "columns": ["labor_id", "cell_mobile_number", "coowner_name"]}], "writes": [{"table": "climber", "columns": ["labor_id", "cell_mobile_number", "coowner_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"school_details\")\nsrc.write.insertInto(\"incarcerated\", overwrite=True)\n", "labels": {"reads": [{"table": "school_details", "columns": null}], "writes": [{"table": "incarcerated", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT text_of_notes, inspectiondate FROM mart.mart_users_di LIMIT 123\")\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO shared_rides_tokyo SELECT customername, initiative_type, grade FROM program_budget WHERE customername > 180\")\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": ["text_of_notes", "inspectiondate"]}, {"table": "program_budget", "columns": ["customername", "initiative_type", "grade"]}], "writes": [{"table": "shared_rides_tokyo", "columns": ["customername", "initiative_type", "grade"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT contract_address, stateid FROM researchgrants LIMIT 297;\nINSERT INTO veteran_stats SELECT change_date, exit_type, refugee_name, analysis_date FROM club WHERE change_date > 428;\nSQL\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["contract_address", "stateid"]}, {"table": "club", "columns": ["change_date", "exit_type", "refugee_name", "analysis_date"]}], "writes": [{"table": "veteran_stats", "columns": ["change_date", "exit_type", "refugee_name", "analysis_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fuelconsumed, incident_name FROM climate_finance_organizations LIMIT 249\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": ["fuelconsumed", "incident_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO city SELECT total_attendance, accommodation_type, medication, feedid FROM social_good_education WHERE total_attendance > 384;\nEOF\n", "labels": {"reads": [{"table": "social_good_education", "columns": ["total_attendance", "accommodation_type", "medication", "feedid"]}], "writes": [{"table": "city", "columns": ["total_attendance", "accommodation_type", "medication", "feedid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO workers SELECT editor_id, transact_date FROM bi.member_point_full WHERE editor_id > 350\"\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": ["editor_id", "transact_date"]}], "writes": [{"table": "workers", "columns": ["editor_id", "transact_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_calendar\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"experts\")\n", "labels": {"reads": [{"table": "ref_calendar", "columns": null}], "writes": [{"table": "experts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table transaction --columns service_name,therapy_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "transaction", "columns": ["service_name", "therapy_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO bike_share SELECT sent_date, materialtype FROM iron WHERE sent_date > 27\"\n", "labels": {"reads": [{"table": "iron", "columns": ["sent_date", "materialtype"]}], "writes": [{"table": "bike_share", "columns": ["sent_date", "materialtype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT startyear, bank_id FROM teacher_pd_hours LIMIT 11\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO solana_transactions SELECT vegan, shipmentid, lot_details FROM dw.dw_member_point_di WHERE vegan > 347\")\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": ["startyear", "bank_id"]}, {"table": "dw.dw_member_point_di", "columns": ["vegan", "shipmentid", "lot_details"]}], "writes": [{"table": "solana_transactions", "columns": ["vegan", "shipmentid", "lot_details"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO membership_data SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attribute_data_type, mediatypeid FROM body_builder LIMIT 165\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "body_builder", "columns": ["attribute_data_type", "mediatypeid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gender_group, mappingname FROM plays_games LIMIT 179\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "plays_games", "columns": ["gender_group", "mappingname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO festival_detail SELECT crossing, g_name, budget_million, ei_category FROM professor WHERE crossing > 495\"\n", "labels": {"reads": [{"table": "professor", "columns": ["crossing", "g_name", "budget_million", "ei_category"]}], "writes": [{"table": "festival_detail", "columns": ["crossing", "g_name", "budget_million", "ei_category"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cloud_issues SELECT a.causeid, b.form_id FROM stg.stg_campaigns_hourly a JOIN haircare_cruelty b ON a.hours_played = b.hours_played\"\n", "labels": {"reads": [{"table": "stg.stg_campaigns_hourly", "columns": null}, {"table": "haircare_cruelty", "columns": null}], "writes": [{"table": "cloud_issues", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_campaigns_df\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO eventdates SELECT waste_type, subject_area_id, i_id FROM tours WHERE waste_type > 449\"\n", "labels": {"reads": [{"table": "tours", "columns": ["waste_type", "subject_area_id", "i_id"]}], "writes": [{"table": "eventdates", "columns": ["waste_type", "subject_area_id", "i_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"causes\")\nsrc.write.insertInto(\"employees\", overwrite=True)\n", "labels": {"reads": [{"table": "causes", "columns": null}], "writes": [{"table": "employees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM trafficviolations\"\n", "labels": {"reads": [{"table": "trafficviolations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO food_production SELECT a.cityid, b.farmer_name FROM user_workouts_march a JOIN attack_outcomes b ON a.eliminated_by = b.eliminated_by\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": null}, {"table": "attack_outcomes", "columns": null}], "writes": [{"table": "food_production", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM canada_tech\"\n", "labels": {"reads": [{"table": "canada_tech", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"wastedata\")\nsrc.write.insertInto(\"buildings\", overwrite=True)\n", "labels": {"reads": [{"table": "wastedata", "columns": null}], "writes": [{"table": "buildings", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ai_for_social_good SELECT farmland_id, volunteerage, studio, contract_end_date FROM useracct WHERE farmland_id > 202\"], check=True)\n", "labels": {"reads": [{"table": "useracct", "columns": ["farmland_id", "volunteerage", "studio", "contract_end_date"]}], "writes": [{"table": "ai_for_social_good", "columns": ["farmland_id", "volunteerage", "studio", "contract_end_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO dailystreams SELECT show_id, routeid, recycling_rate FROM stg.stg_users_full WHERE show_id > 402\")\n", "labels": {"reads": [{"table": "stg.stg_users_full", "columns": ["show_id", "routeid", "recycling_rate"]}], "writes": [{"table": "dailystreams", "columns": ["show_id", "routeid", "recycling_rate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"forest_species\").toPandas()\ndf[[\"session_name\", \"improvement\"]].to_sql(\"journal\", engine, index=False)\n", "labels": {"reads": [{"table": "forest_species", "columns": null}], "writes": [{"table": "journal", "columns": ["session_name", "improvement"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO member_of_club SELECT museumname, vehicle_flight_number, prof_office, hours_served FROM students WHERE museumname > 130\"\n", "labels": {"reads": [{"table": "students", "columns": ["museumname", "vehicle_flight_number", "prof_office", "hours_served"]}], "writes": [{"table": "member_of_club", "columns": ["museumname", "vehicle_flight_number", "prof_office", "hours_served"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO gameplatforms SELECT habitat_id, explainability_score, duration FROM travel_advisory WHERE habitat_id > 262\"], check=True)\n", "labels": {"reads": [{"table": "travel_advisory", "columns": ["habitat_id", "explainability_score", "duration"]}], "writes": [{"table": "gameplatforms", "columns": ["habitat_id", "explainability_score", "duration"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT incident_name, draft_pick_number FROM highways\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"satellite_missions_large\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "highways", "columns": ["incident_name", "draft_pick_number"]}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi_shipments_daily SELECT a.book_title, b.strategy FROM organisations a JOIN hires b ON a.premises_type = b.premises_type\"\n", "labels": {"reads": [{"table": "organisations", "columns": null}, {"table": "hires", "columns": null}], "writes": [{"table": "bi_shipments_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO virtual_visitors SELECT donation_year, judge_id, complaint_date FROM conditions WHERE donation_year > 5\"], check=True)\n", "labels": {"reads": [{"table": "conditions", "columns": ["donation_year", "judge_id", "complaint_date"]}], "writes": [{"table": "virtual_visitors", "columns": ["donation_year", "judge_id", "complaint_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model solana_transactions depends on test_drives\ndbt build --models solana_transactions --vars 'source: test_drives'\n", "labels": {"reads": [{"table": "test_drives", "columns": null}], "writes": [{"table": "solana_transactions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.bias_score > 313).all()\n# src table: bi_refunds_daily\nengine.execute(\"INSERT INTO district_schools SELECT * FROM bi_refunds_daily\")\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": null}], "writes": [{"table": "district_schools", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT area_id, document_description FROM dwd.dwd_cart_item_di LIMIT 446\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": ["area_id", "document_description"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT trader_id, warehouseid FROM experience LIMIT 312\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO ods.ods_campaigns_df SELECT stu_hrs, score, facid FROM genderdistribution WHERE stu_hrs > 192\")\n", "labels": {"reads": [{"table": "experience", "columns": ["trader_id", "warehouseid"]}, {"table": "genderdistribution", "columns": ["stu_hrs", "score", "facid"]}], "writes": [{"table": "ods.ods_campaigns_df", "columns": ["stu_hrs", "score", "facid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tracks\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"economic_diversification_efforts\")\n", "labels": {"reads": [{"table": "tracks", "columns": null}], "writes": [{"table": "economic_diversification_efforts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table broadband_providers --columns minename,investment_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "broadband_providers", "columns": ["minename", "investment_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads_orders SELECT document_type_description, start_speed, soil_moisture FROM recycled_polyester WHERE document_type_description > 497\"\n", "labels": {"reads": [{"table": "recycled_polyester", "columns": ["document_type_description", "start_speed", "soil_moisture"]}], "writes": [{"table": "ads_orders", "columns": ["document_type_description", "start_speed", "soil_moisture"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"pharmasales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "pharmasales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT prereq_id, booking_start_date FROM customer_transactions\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ndf.to_sql(\"tracklists\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customer_transactions", "columns": ["prereq_id", "booking_start_date"]}], "writes": [{"table": "tracklists", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM community.donations\"\n", "labels": {"reads": [{"table": "community.donations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"philadelphia_police_emergencies\").toPandas()\ndf[[\"advisor\", \"clean_jerk\"]].to_sql(\"bridgeconstruction\", engine, index=False)\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": [{"table": "bridgeconstruction", "columns": ["advisor", "clean_jerk"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd.vendors SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM evidence_based_policies\", conn)\ndf.to_sql(\"sales_by_quarter\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": null}], "writes": [{"table": "sales_by_quarter", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table defense_contracts_v2 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO wine SELECT a.fine, b.device_id FROM dws.dws_coupon_use_hourly a JOIN daily_industrial_water_usage b ON a.passenger_name = b.passenger_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_hourly", "columns": null}, {"table": "daily_industrial_water_usage", "columns": null}], "writes": [{"table": "wine", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM film_category\"\n", "labels": {"reads": [{"table": "film_category", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table fairtradefactories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fairtradefactories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM state_water_usage\"\n", "labels": {"reads": [{"table": "state_water_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO heritagesites SELECT * FROM legacy\ncur.execute(\"SELECT pettype, meter_300 FROM nyc_subway LIMIT 184\")\n", "labels": {"reads": [{"table": "nyc_subway", "columns": ["pettype", "meter_300"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_source(ctx, \"ads.events\")\npush_to_target(df, \"makeup_sales\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.events", "columns": null}], "writes": [{"table": "makeup_sales", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT county_name, jobtitle FROM nomination LIMIT 94;\nINSERT INTO refugee_support SELECT cuisine, transaction_type_code, functional_area_code, team_id_br FROM road_construction WHERE cuisine > 54;\nSQL\n", "labels": {"reads": [{"table": "nomination", "columns": ["county_name", "jobtitle"]}, {"table": "road_construction", "columns": ["cuisine", "transaction_type_code", "functional_area_code", "team_id_br"]}], "writes": [{"table": "refugee_support", "columns": ["cuisine", "transaction_type_code", "functional_area_code", "team_id_br"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"tweets\")\npersist_to_sink(df, \"weapons\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tweets", "columns": null}], "writes": [{"table": "weapons", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT advisoryid, fabrictype FROM stg.stg_campaigns LIMIT 367\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO open_pedagogy SELECT therapy_type, funding_round_id FROM state_energy WHERE therapy_type > 229\")\n", "labels": {"reads": [{"table": "stg.stg_campaigns", "columns": ["advisoryid", "fabrictype"]}, {"table": "state_energy", "columns": ["therapy_type", "funding_round_id"]}], "writes": [{"table": "open_pedagogy", "columns": ["therapy_type", "funding_round_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO precision_farming_imagery SELECT orgid, meter_200, game, artifact_weight FROM storage_tech WHERE orgid > 81\"\n", "labels": {"reads": [{"table": "storage_tech", "columns": ["orgid", "meter_200", "game", "artifact_weight"]}], "writes": [{"table": "precision_farming_imagery", "columns": ["orgid", "meter_200", "game", "artifact_weight"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT defendant_id, task_id FROM artprograms\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"pollution_control_initiatives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "artprograms", "columns": ["defendant_id", "task_id"]}], "writes": [{"table": "pollution_control_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT permitid, purchase_id FROM race\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"stg.risk_score_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "race", "columns": ["permitid", "purchase_id"]}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT model_id, cultivatorname FROM financial_capability_programs LIMIT 137\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO schoolc SELECT sustainabilityrating, capital, dock_id FROM autoshow WHERE sustainabilityrating > 323\")\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": ["model_id", "cultivatorname"]}, {"table": "autoshow", "columns": ["sustainabilityrating", "capital", "dock_id"]}], "writes": [{"table": "schoolc", "columns": ["sustainabilityrating", "capital", "dock_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO resilience_infrastructure SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT treatment_year, business_id FROM precipitation_data\", engine)\nimport logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"city_tech\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "precipitation_data", "columns": ["treatment_year", "business_id"]}], "writes": [{"table": "city_tech", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO bike_share SELECT is_recycled, host_country FROM aquatic_farms WHERE is_recycled > 169;\nEOF\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["is_recycled", "host_country"]}], "writes": [{"table": "bike_share", "columns": ["is_recycled", "host_country"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vehicle_maintenance (energy_efficiency_savings, province) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vehicle_maintenance", "columns": ["energy_efficiency_savings", "province"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"wildlife\")\nupsert_to_warehouse(df, \"greenbuildings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "wildlife", "columns": null}], "writes": [{"table": "greenbuildings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO concert SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"displaced_people\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"wearable_metrics\")\n", "labels": {"reads": [{"table": "displaced_people", "columns": null}], "writes": [{"table": "wearable_metrics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"college\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"crops_table\")\n", "labels": {"reads": [{"table": "college", "columns": null}], "writes": [{"table": "crops_table", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"school\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "school", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"forest\")\npush_to_warehouse(df, \"defense_project_timelines\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "forest", "columns": null}], "writes": [{"table": "defense_project_timelines", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO habitat3 SELECT * FROM legacy\ncur.execute(\"SELECT experience, draft_pick_number FROM communitypolicing LIMIT 296\")\n", "labels": {"reads": [{"table": "communitypolicing", "columns": ["experience", "draft_pick_number"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vaccination_status, feature_details FROM tech_accessibility_funding\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"fuel_consumption\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": ["vaccination_status", "feature_details"]}], "writes": [{"table": "fuel_consumption", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO vulnerabilities SELECT a.extraction_date, b.user_id FROM clothingitems a JOIN mart_orders_di b ON a.arrival = b.arrival\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}, {"table": "mart_orders_di", "columns": null}], "writes": [{"table": "vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"opioid_overdoses\")\nsrc.write.insertInto(\"biodiversity\", overwrite=True)\n", "labels": {"reads": [{"table": "opioid_overdoses", "columns": null}], "writes": [{"table": "biodiversity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO dwd.dwd_campaigns_df SELECT school, archeologist FROM diplomacy_events WHERE school > 337\")\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": ["school", "archeologist"]}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": ["school", "archeologist"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT daily_co2_emission, dribbling FROM menu_engineering LIMIT 307\")\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO sales_2 SELECT lat, tour_name FROM esportsteamsafrica WHERE lat > 104\")\n", "labels": {"reads": [{"table": "menu_engineering", "columns": ["daily_co2_emission", "dribbling"]}, {"table": "esportsteamsafrica", "columns": ["lat", "tour_name"]}], "writes": [{"table": "sales_2", "columns": ["lat", "tour_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO local_impact_japan SELECT a.order_status, b.health_equity_metric_3 FROM broadband_subscribers a JOIN fertilizer_usage b ON a.ranking = b.ranking\"\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": null}, {"table": "fertilizer_usage", "columns": null}], "writes": [{"table": "local_impact_japan", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO global_tournament (line_id, water_temp) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "global_tournament", "columns": ["line_id", "water_temp"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO supply_chain SELECT founder_identity, strat_id, missing_data FROM drug_approval WHERE founder_identity > 254\"\n", "labels": {"reads": [{"table": "drug_approval", "columns": ["founder_identity", "strat_id", "missing_data"]}], "writes": [{"table": "supply_chain", "columns": ["founder_identity", "strat_id", "missing_data"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.member_point_df\"\n", "labels": {"reads": [{"table": "stg.member_point_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model flights depends on rigs\ndbt build --models flights --vars '{\"source_table\":\"rigs\"}'\n", "labels": {"reads": [{"table": "rigs", "columns": null}], "writes": [{"table": "flights", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO climate_finance_asia SELECT fish_count, beds FROM energy_storage WHERE fish_count > 246\"], check=True)\n", "labels": {"reads": [{"table": "energy_storage", "columns": ["fish_count", "beds"]}], "writes": [{"table": "climate_finance_asia", "columns": ["fish_count", "beds"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ca_menu_items\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi_refunds_daily\")\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}], "writes": [{"table": "bi_refunds_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ocean_depths SELECT train_id, museum_name, emp_hiredate, time_minute FROM diplomacy_events WHERE train_id > 292\"\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": ["train_id", "museum_name", "emp_hiredate", "time_minute"]}], "writes": [{"table": "ocean_depths", "columns": ["train_id", "museum_name", "emp_hiredate", "time_minute"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"biosensors.readings\")\nsave_to_output(df, \"genetics.crispr\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "biosensors.readings", "columns": null}], "writes": [{"table": "genetics.crispr", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO passengers SELECT * FROM legacy\ncur.execute(\"SELECT closure_authorised_by_staff_id, productid FROM packages LIMIT 149\")\n", "labels": {"reads": [{"table": "packages", "columns": ["closure_authorised_by_staff_id", "productid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"dw.dw_member_point_di\")\npush_to_target(df, \"device\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": null}], "writes": [{"table": "device", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_member_point_di SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ref_detention_type SELECT trader_id, fault_log_entry_datetime, manager_name FROM dw.dw_users_di WHERE trader_id > 144\"], check=True)\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": ["trader_id", "fault_log_entry_datetime", "manager_name"]}], "writes": [{"table": "ref_detention_type", "columns": ["trader_id", "fault_log_entry_datetime", "manager_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"document_types\").toPandas()\ndf[[\"program_date\", \"device\"]].to_sql(\"roller_coaster\", engine, index=False)\n", "labels": {"reads": [{"table": "document_types", "columns": null}], "writes": [{"table": "roller_coaster", "columns": ["program_date", "device"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vehicledata SELECT billing_city, regulation, is_sustainable FROM drug_approvals WHERE billing_city > 17\"\n", "labels": {"reads": [{"table": "drug_approvals", "columns": ["billing_city", "regulation", "is_sustainable"]}], "writes": [{"table": "vehicledata", "columns": ["billing_city", "regulation", "is_sustainable"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO patient_outcomes SELECT * FROM legacy\ncur.execute(\"SELECT item_type, communityid FROM ads_users_hourly LIMIT 56\")\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": ["item_type", "communityid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tour_guides (vulnerability_score, temporary_acting) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tour_guides", "columns": ["vulnerability_score", "temporary_acting"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO accessibility_audits SELECT * FROM legacy\ncur.execute(\"SELECT breed, staff_details FROM charging_stations LIMIT 322\")\n", "labels": {"reads": [{"table": "charging_stations", "columns": ["breed", "staff_details"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO audience SELECT fan_age, incidenttype, f_id, customer_number FROM ocean_floor_depth WHERE fan_age > 319\"\n", "labels": {"reads": [{"table": "ocean_floor_depth", "columns": ["fan_age", "incidenttype", "f_id", "customer_number"]}], "writes": [{"table": "audience", "columns": ["fan_age", "incidenttype", "f_id", "customer_number"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT financial_wellbeing_score, donator_name FROM dws_events_di LIMIT 430\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO movie SELECT member, cinema_id, process_id FROM movies WHERE member > 259\")\n", "labels": {"reads": [{"table": "dws_events_di", "columns": ["financial_wellbeing_score", "donator_name"]}, {"table": "movies", "columns": ["member", "cinema_id", "process_id"]}], "writes": [{"table": "movie", "columns": ["member", "cinema_id", "process_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"draft_copies\")\ndump_to_sink(df, \"climate_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}], "writes": [{"table": "climate_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cybersecurity_strategies SELECT fouls, program_name, precip FROM coal WHERE fouls > 434\"\n", "labels": {"reads": [{"table": "coal", "columns": ["fouls", "program_name", "precip"]}], "writes": [{"table": "cybersecurity_strategies", "columns": ["fouls", "program_name", "precip"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT negotiation_date, ai_model FROM membership_data LIMIT 127\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "membership_data", "columns": ["negotiation_date", "ai_model"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO cargo_data SELECT a.num_shariah_compliant_investments, b.tier FROM carbon_offset_programs a JOIN shared_rides_tokyo b ON a.program_date = b.program_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}, {"table": "shared_rides_tokyo", "columns": null}], "writes": [{"table": "cargo_data", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO benefits_overpayments SELECT a.attraction_type_description, b.driverid FROM ads_users_hourly a JOIN shelters b ON a.effective_date = b.effective_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": null}, {"table": "shelters", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.mart_products_hourly --columns fair_labor,policy_description --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_products_hourly", "columns": ["fair_labor", "policy_description"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO teacher_development_race SELECT session_language, oppose_rate, vaccination_status, wellid FROM member_of WHERE session_language > 207\"\n", "labels": {"reads": [{"table": "member_of", "columns": ["session_language", "oppose_rate", "vaccination_status", "wellid"]}], "writes": [{"table": "teacher_development_race", "columns": ["session_language", "oppose_rate", "vaccination_status", "wellid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"uel_top10\")\nwrite_to_target(df, \"product_ingredient\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "uel_top10", "columns": null}], "writes": [{"table": "product_ingredient", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wells --columns artist_gender,online_dispute_resolution --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wells", "columns": ["artist_gender", "online_dispute_resolution"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sponsorship_donations SELECT a.host_city, b.start_therapy FROM call_volume a JOIN hospital_equipment b ON a.date_and_date = b.date_and_date\"\n", "labels": {"reads": [{"table": "call_volume", "columns": null}, {"table": "hospital_equipment", "columns": null}], "writes": [{"table": "sponsorship_donations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO election SELECT cruelty_free, location, pid, taskid FROM satisfaction WHERE cruelty_free > 135\")\n", "labels": {"reads": [{"table": "satisfaction", "columns": ["cruelty_free", "location", "pid", "taskid"]}], "writes": [{"table": "election", "columns": ["cruelty_free", "location", "pid", "taskid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewableenergy\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"building\")\n", "labels": {"reads": [{"table": "renewableenergy", "columns": null}], "writes": [{"table": "building", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"view_unit_status\")\nexport_to_store(df, \"haircare_cruelty\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "view_unit_status", "columns": null}], "writes": [{"table": "haircare_cruelty", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO workout SELECT vaccination_status, cargo_type, part_id FROM geological_survey WHERE vaccination_status > 257\"\n", "labels": {"reads": [{"table": "geological_survey", "columns": ["vaccination_status", "cargo_type", "part_id"]}], "writes": [{"table": "workout", "columns": ["vaccination_status", "cargo_type", "part_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO military_aircraft_maintenance SELECT goal_date, inspection_time FROM farmers_india WHERE goal_date > 298\"\n", "labels": {"reads": [{"table": "farmers_india", "columns": ["goal_date", "inspection_time"]}], "writes": [{"table": "military_aircraft_maintenance", "columns": ["goal_date", "inspection_time"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO chemical_production_5 SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO festivals SELECT a.releasedate, b.publication_year FROM intelligence_agents a JOIN music_database b ON a.meter_200 = b.meter_200\"\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}, {"table": "music_database", "columns": null}], "writes": [{"table": "festivals", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table studentaccommodations --columns nation,case_burden --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "studentaccommodations", "columns": ["nation", "case_burden"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_risk_score_df\", conn)\ndf.to_sql(\"climateresearch\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_risk_score_df", "columns": null}], "writes": [{"table": "climateresearch", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stops (astronaut_name, mountain_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stops", "columns": ["astronaut_name", "mountain_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table bi.inventory_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.inventory_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO enzyme SELECT products_last_year, project FROM disaster_zones WHERE products_last_year > 201;\nEOF\n", "labels": {"reads": [{"table": "disaster_zones", "columns": ["products_last_year", "project"]}], "writes": [{"table": "enzyme", "columns": ["products_last_year", "project"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT completion_year, energy_efficiency_kwh_m2_year FROM foodsafetyrecords LIMIT 389\")\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO dwd.dwd_vendors SELECT data_usage, crispr_id, enable_third_party_ads FROM membership_register_branch WHERE data_usage > 416\")\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": ["completion_year", "energy_efficiency_kwh_m2_year"]}, {"table": "membership_register_branch", "columns": ["data_usage", "crispr_id", "enable_third_party_ads"]}], "writes": [{"table": "dwd.dwd_vendors", "columns": ["data_usage", "crispr_id", "enable_third_party_ads"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO laptimes SELECT partner, aid, cause_name FROM cybersecurity_strategies WHERE partner > 482\"], check=True)\n", "labels": {"reads": [{"table": "cybersecurity_strategies", "columns": ["partner", "aid", "cause_name"]}], "writes": [{"table": "laptimes", "columns": ["partner", "aid", "cause_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO hotel_tech_adoption SELECT donor_id, product_price, client_first_name FROM climate_adaptation_projects WHERE donor_id > 35\")\n", "labels": {"reads": [{"table": "climate_adaptation_projects", "columns": ["donor_id", "product_price", "client_first_name"]}], "writes": [{"table": "hotel_tech_adoption", "columns": ["donor_id", "product_price", "client_first_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT led_by, document_type_code FROM ai_safety_incidents LIMIT 192\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": ["led_by", "document_type_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO efforts (job, delivery_time) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "efforts", "columns": ["job", "delivery_time"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table inclusive_housing --target-dir /tmp/land\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO exhibition_visitors (enzyme_id, financially_capable) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "exhibition_visitors", "columns": ["enzyme_id", "financially_capable"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO ingredientsvegancrueltyfree SELECT a.productid, b.endtime FROM shariahfinance a JOIN dwd.dwd_payments_full b ON a.fiscal_year = b.fiscal_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "shariahfinance", "columns": null}, {"table": "dwd.dwd_payments_full", "columns": null}], "writes": [{"table": "ingredientsvegancrueltyfree", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd.dwd_orders_daily SELECT prof_num, eia_date, cargoid FROM climate_data WHERE prof_num > 57\"\n", "labels": {"reads": [{"table": "climate_data", "columns": ["prof_num", "eia_date", "cargoid"]}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": ["prof_num", "eia_date", "cargoid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT authorder, dormid FROM dwd.dwd_campaigns LIMIT 433;\nINSERT INTO climate_adaptation_re SELECT menu_item_id, grade, founded FROM inclusivehousing.affordablehousing WHERE menu_item_id > 197;\nSQL\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": ["authorder", "dormid"]}, {"table": "inclusivehousing.affordablehousing", "columns": ["menu_item_id", "grade", "founded"]}], "writes": [{"table": "climate_adaptation_re", "columns": ["menu_item_id", "grade", "founded"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO virtual_tour_engagement SELECT breed, day_of_week FROM journal WHERE breed > 281\"\n", "labels": {"reads": [{"table": "journal", "columns": ["breed", "day_of_week"]}], "writes": [{"table": "virtual_tour_engagement", "columns": ["breed", "day_of_week"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sustainable_warehouses SELECT annual_revenue, fault_short_name, deliveryid, paritystatus FROM spacemissions WHERE annual_revenue > 53\"\n", "labels": {"reads": [{"table": "spacemissions", "columns": ["annual_revenue", "fault_short_name", "deliveryid", "paritystatus"]}], "writes": [{"table": "sustainable_warehouses", "columns": ["annual_revenue", "fault_short_name", "deliveryid", "paritystatus"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_coupon_use_hourly SELECT participation_id, paintingid FROM manager_award WHERE participation_id > 403\"], check=True)\n", "labels": {"reads": [{"table": "manager_award", "columns": ["participation_id", "paintingid"]}], "writes": [{"table": "stg.stg_coupon_use_hourly", "columns": ["participation_id", "paintingid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table hotel_ratings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO coal_reserves SELECT a.recipe_id, b.no_of_customers FROM virtual_tourism a JOIN farmers b ON a.product_category_code = b.product_category_code\"\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": null}, {"table": "farmers", "columns": null}], "writes": [{"table": "coal_reserves", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.users\")\nsrc.write.insertInto(\"ocean\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.users", "columns": null}], "writes": [{"table": "ocean", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.unit_price > 138).all()\n# src table: ref_document_status\nengine.execute(\"INSERT INTO trends_2022 SELECT * FROM ref_document_status\")\n", "labels": {"reads": [{"table": "ref_document_status", "columns": null}], "writes": [{"table": "trends_2022", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT productlaunchdate, total_cost FROM match\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"video_content\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "match", "columns": ["productlaunchdate", "total_cost"]}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.orders_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ods.ods_users_di\")\n", "labels": {"reads": [{"table": "ads.orders_daily", "columns": null}], "writes": [{"table": "ods.ods_users_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"paris_train\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw.dw_inventory_delta\")\n", "labels": {"reads": [{"table": "paris_train", "columns": null}], "writes": [{"table": "dw.dw_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"intel_ops\").toPandas()\ndf[[\"yield\", \"enzyme_id\"]].to_sql(\"education\", engine, index=False)\n", "labels": {"reads": [{"table": "intel_ops", "columns": null}], "writes": [{"table": "education", "columns": ["yield", "enzyme_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO discipline_enrollments SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT item_size, parent_organization_id FROM device_usage LIMIT 184\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "device_usage", "columns": ["item_size", "parent_organization_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.events SELECT objectnumber, ironquantity, time_id FROM space_missions_2 WHERE objectnumber > 239\"], check=True)\n", "labels": {"reads": [{"table": "space_missions_2", "columns": ["objectnumber", "ironquantity", "time_id"]}], "writes": [{"table": "ads.events", "columns": ["objectnumber", "ironquantity", "time_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_member_point_di SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM defense_projects_sales\"\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"garmentproduction\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"journal\")\n", "labels": {"reads": [{"table": "garmentproduction", "columns": null}], "writes": [{"table": "journal", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.risk_score_df\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO individual SELECT * FROM legacy\ncur.execute(\"SELECT network_name, track_id FROM crops LIMIT 193\")\n", "labels": {"reads": [{"table": "crops", "columns": ["network_name", "track_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO course_authors_and_tutors SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO employeedata SELECT eco_friendly, destroyed_by_employee_id, implementation_year FROM vessel_safety WHERE eco_friendly > 78;\nEOF\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["eco_friendly", "destroyed_by_employee_id", "implementation_year"]}], "writes": [{"table": "employeedata", "columns": ["eco_friendly", "destroyed_by_employee_id", "implementation_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ticket_sales SELECT market_details, annual_carbon_offsets, stuid, consumer_id FROM daily_articles_by_category WHERE market_details > 183\"\n", "labels": {"reads": [{"table": "daily_articles_by_category", "columns": ["market_details", "annual_carbon_offsets", "stuid", "consumer_id"]}], "writes": [{"table": "ticket_sales", "columns": ["market_details", "annual_carbon_offsets", "stuid", "consumer_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO reo_production SELECT content, well_name FROM communication_scores WHERE content > 264\"\n", "labels": {"reads": [{"table": "communication_scores", "columns": ["content", "well_name"]}], "writes": [{"table": "reo_production", "columns": ["content", "well_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"unionnegotiations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO policy_feedback SELECT num_of_factories, spf_level, museum_id, resolutiondate FROM jp_schema.policy_areas WHERE num_of_factories > 414\"\n", "labels": {"reads": [{"table": "jp_schema.policy_areas", "columns": ["num_of_factories", "spf_level", "museum_id", "resolutiondate"]}], "writes": [{"table": "policy_feedback", "columns": ["num_of_factories", "spf_level", "museum_id", "resolutiondate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT totalprice, co2_offset_amount FROM student_program_mapping LIMIT 378;\nINSERT INTO communitydevelopment SELECT school_id, month FROM government.city WHERE school_id > 247;\nSQL\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": ["totalprice", "co2_offset_amount"]}, {"table": "government.city", "columns": ["school_id", "month"]}], "writes": [{"table": "communitydevelopment", "columns": ["school_id", "month"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT royal_family_id, individual_first_name FROM cycling LIMIT 159\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "cycling", "columns": ["royal_family_id", "individual_first_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO autoshow SELECT range, delivery_date FROM mineral_extraction WHERE range > 163\"\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": ["range", "delivery_date"]}], "writes": [{"table": "autoshow", "columns": ["range", "delivery_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table donorgender --target-dir /tmp/land\n", "labels": {"reads": [{"table": "donorgender", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM animal_budget\", conn)\ndf.to_sql(\"military_personnel_africa\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "animal_budget", "columns": null}], "writes": [{"table": "military_personnel_africa", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO bi.bi_exposure_hourly SELECT artist_id, drought_id FROM circular_economy WHERE artist_id > 70\")\n", "labels": {"reads": [{"table": "circular_economy", "columns": ["artist_id", "drought_id"]}], "writes": [{"table": "bi.bi_exposure_hourly", "columns": ["artist_id", "drought_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"minor_in\")\nsrc.write.insertInto(\"gamestats\", overwrite=True)\n", "labels": {"reads": [{"table": "minor_in", "columns": null}], "writes": [{"table": "gamestats", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.member_point\", conn)\ndf.to_sql(\"reservoirs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.member_point", "columns": null}], "writes": [{"table": "reservoirs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model manufacturers depends on provinces\ndbt run --select manufacturers --vars '{\"src\":\"provinces\"}'\n", "labels": {"reads": [{"table": "provinces", "columns": null}], "writes": [{"table": "manufacturers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT biome_id, cultivatorname FROM port_office LIMIT 13\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "port_office", "columns": ["biome_id", "cultivatorname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tourismproviders\", conn)\ndf.to_sql(\"concert_sales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tourismproviders", "columns": null}], "writes": [{"table": "concert_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vessel_registry SELECT a.attraction_type_description, b.drought_id FROM prices a JOIN cultivators b ON a.gamepreference = b.gamepreference\"\n", "labels": {"reads": [{"table": "prices", "columns": null}, {"table": "cultivators", "columns": null}], "writes": [{"table": "vessel_registry", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO membership SELECT official_native_language, building_manager, routeid FROM bi.bi_campaigns_delta WHERE official_native_language > 284\"\n", "labels": {"reads": [{"table": "bi.bi_campaigns_delta", "columns": ["official_native_language", "building_manager", "routeid"]}], "writes": [{"table": "membership", "columns": ["official_native_language", "building_manager", "routeid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO shops SELECT * FROM legacy\ncur.execute(\"SELECT subscriber_type, circuitid FROM ai_safety LIMIT 49\")\n", "labels": {"reads": [{"table": "ai_safety", "columns": ["subscriber_type", "circuitid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"network_infrastructure\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"volunteer_hours\")\n", "labels": {"reads": [{"table": "network_infrastructure", "columns": null}], "writes": [{"table": "volunteer_hours", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_inventory_di\")\nsrc.write.insertInto(\"road_construction\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": null}], "writes": [{"table": "road_construction", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT strain_type, review_id FROM ads.ads_shipments_delta LIMIT 140\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO workforce SELECT home_city, party, playlist_id FROM humanitarian_assistance WHERE home_city > 241\")\n", "labels": {"reads": [{"table": "ads.ads_shipments_delta", "columns": ["strain_type", "review_id"]}, {"table": "humanitarian_assistance", "columns": ["home_city", "party", "playlist_id"]}], "writes": [{"table": "workforce", "columns": ["home_city", "party", "playlist_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.asset_acquired_date > 5).all()\n# src table: mart.clicks\nengine.execute(\"INSERT INTO farm_competition SELECT * FROM mart.clicks\")\n", "labels": {"reads": [{"table": "mart.clicks", "columns": null}], "writes": [{"table": "farm_competition", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO routes (faculty_id, staff_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "routes", "columns": ["faculty_id", "staff_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT attendee_race, shipmentid FROM happy_hour LIMIT 97\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ingredients SELECT precipitation, num_sessions, co_id FROM multimodal_trips WHERE precipitation > 292\")\n", "labels": {"reads": [{"table": "happy_hour", "columns": ["attendee_race", "shipmentid"]}, {"table": "multimodal_trips", "columns": ["precipitation", "num_sessions", "co_id"]}], "writes": [{"table": "ingredients", "columns": ["precipitation", "num_sessions", "co_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cmi_cross_ref_id, text_of_notes FROM ads.vendors_delta LIMIT 464\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO restaurants_tx SELECT programtype, home_team, next_maintenance, recruitername FROM navalvessels WHERE programtype > 306\")\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": ["cmi_cross_ref_id", "text_of_notes"]}, {"table": "navalvessels", "columns": ["programtype", "home_team", "next_maintenance", "recruitername"]}], "writes": [{"table": "restaurants_tx", "columns": ["programtype", "home_team", "next_maintenance", "recruitername"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attribute_definitions\").toPandas()\ndf[[\"signup_date\", \"provider_id\"]].to_sql(\"stg.stg_coupon_use_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": null}], "writes": [{"table": "stg.stg_coupon_use_hourly", "columns": ["signup_date", "provider_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dispensary, song_id FROM device_accessibility\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"inspectiondata\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "device_accessibility", "columns": ["dispensary", "song_id"]}], "writes": [{"table": "inspectiondata", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_refunds_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cosmetic_formula\")\n", "labels": {"reads": [{"table": "dws.dws_refunds_daily", "columns": null}], "writes": [{"table": "cosmetic_formula", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT content, total_horses FROM ads LIMIT 258;\nINSERT INTO province.human_rights_data SELECT line_1_number_building, dob FROM lenders WHERE line_1_number_building > 88;\nSQL\n", "labels": {"reads": [{"table": "ads", "columns": ["content", "total_horses"]}, {"table": "lenders", "columns": ["line_1_number_building", "dob"]}], "writes": [{"table": "province.human_rights_data", "columns": ["line_1_number_building", "dob"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO hotel_tech_adoptions SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"contract_negotiations_un\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ads_sessions_di depends on certificate\ndbt build --select ads_sessions_di --vars '{\"source_table\":\"certificate\"}'\n", "labels": {"reads": [{"table": "certificate", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO droughthistory SELECT class_section, entrydate FROM gene WHERE class_section > 214\"\n", "labels": {"reads": [{"table": "gene", "columns": ["class_section", "entrydate"]}], "writes": [{"table": "droughthistory", "columns": ["class_section", "entrydate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT show_times_per_day, unitprice FROM exhibition_record LIMIT 65\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO customer SELECT retailer_id, highscore FROM bridgerainfall WHERE retailer_id > 390\")\n", "labels": {"reads": [{"table": "exhibition_record", "columns": ["show_times_per_day", "unitprice"]}, {"table": "bridgerainfall", "columns": ["retailer_id", "highscore"]}], "writes": [{"table": "customer", "columns": ["retailer_id", "highscore"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO wind_farms (graduate, deliverydate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "wind_farms", "columns": ["graduate", "deliverydate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO virtual_visitors SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO firestations SELECT sale_id, news_outlet, updated_at FROM regional_railways WHERE sale_id > 59;\nEOF\n", "labels": {"reads": [{"table": "regional_railways", "columns": ["sale_id", "news_outlet", "updated_at"]}], "writes": [{"table": "firestations", "columns": ["sale_id", "news_outlet", "updated_at"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM medical_facilities\"\n", "labels": {"reads": [{"table": "medical_facilities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT conferencename, avg_yield FROM military_equipment_maintenance LIMIT 175\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": ["conferencename", "avg_yield"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO arctic_sightings SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO supplier_addresses SELECT party_id, tournament_id FROM tencel_sources WHERE party_id > 474;\nEOF\n", "labels": {"reads": [{"table": "tencel_sources", "columns": ["party_id", "tournament_id"]}], "writes": [{"table": "supplier_addresses", "columns": ["party_id", "tournament_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model artcontributors depends on strainlabresults\ndbt run -s artcontributors --vars '{\"src\":\"strainlabresults\"}'\n", "labels": {"reads": [{"table": "strainlabresults", "columns": null}], "writes": [{"table": "artcontributors", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"jupiter_spacecraft\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"workercontactinfo\")\n", "labels": {"reads": [{"table": "jupiter_spacecraft", "columns": null}], "writes": [{"table": "workercontactinfo", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM production\", conn)\ndf.to_sql(\"ref_detention_type\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "production", "columns": null}], "writes": [{"table": "ref_detention_type", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO recall_reports SELECT a.county_name, b.num_courses FROM ads.events a JOIN militarybases b ON a.mental_health_status = b.mental_health_status\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.events", "columns": null}, {"table": "militarybases", "columns": null}], "writes": [{"table": "recall_reports", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO west_providers SELECT * FROM legacy\ncur.execute(\"SELECT chw_id, exploited FROM crimes LIMIT 154\")\n", "labels": {"reads": [{"table": "crimes", "columns": ["chw_id", "exploited"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sustainability_fact SELECT dock_count, membername FROM contract_negotiations WHERE dock_count > 112\"\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": ["dock_count", "membername"]}], "writes": [{"table": "sustainability_fact", "columns": ["dock_count", "membername"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bustrips SELECT business_size, supplychainid, province_name FROM faculty WHERE business_size > 169\"\n", "labels": {"reads": [{"table": "faculty", "columns": ["business_size", "supplychainid", "province_name"]}], "writes": [{"table": "bustrips", "columns": ["business_size", "supplychainid", "province_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO contractorsales SELECT ll_hours, transportation_method, organisation_type_description, amount_paid FROM shrimp_farms WHERE ll_hours > 73\")\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["ll_hours", "transportation_method", "organisation_type_description", "amount_paid"]}], "writes": [{"table": "contractorsales", "columns": ["ll_hours", "transportation_method", "organisation_type_description", "amount_paid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM busmaintenance\"\n", "labels": {"reads": [{"table": "busmaintenance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.crop > 169).all()\n# src table: region_stats\nengine.execute(\"INSERT INTO product_sales SELECT * FROM region_stats\")\n", "labels": {"reads": [{"table": "region_stats", "columns": null}], "writes": [{"table": "product_sales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vehicle_sales\", conn)\ndf.to_sql(\"budget\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vehicle_sales", "columns": null}], "writes": [{"table": "budget", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_detention_type\").toPandas()\ndf[[\"pages_per_minute_color\", \"bedroom_count\"]].to_sql(\"participates_in\", engine, index=False)\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": null}], "writes": [{"table": "participates_in", "columns": ["pages_per_minute_color", "bedroom_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"territory.human_rights_data\")\nsrc.write.insertInto(\"company\", overwrite=True)\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": null}], "writes": [{"table": "company", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ods.ods_coupon_use_di SELECT undergraduate, donorid, product_details FROM mart.member_point_df WHERE undergraduate > 132\")\n", "labels": {"reads": [{"table": "mart.member_point_df", "columns": ["undergraduate", "donorid", "product_details"]}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": ["undergraduate", "donorid", "product_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO operations (serving_size, other_hotel_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "operations", "columns": ["serving_size", "other_hotel_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"influencers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"size\")\n", "labels": {"reads": [{"table": "influencers", "columns": null}], "writes": [{"table": "size", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table authenticationlogs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "authenticationlogs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO ods.ods_events_daily SELECT a.num_transactions, b.eco_certified FROM violations a JOIN ocean_depths b ON a.issue_count = b.issue_count\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "violations", "columns": null}, {"table": "ocean_depths", "columns": null}], "writes": [{"table": "ods.ods_events_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO restorative_justice_programs SELECT donator_name, innovation FROM dwd.dwd_orders_di WHERE donator_name > 42\"\n", "labels": {"reads": [{"table": "dwd.dwd_orders_di", "columns": ["donator_name", "innovation"]}], "writes": [{"table": "restorative_justice_programs", "columns": ["donator_name", "innovation"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT analysis_date, passenger_count FROM routes LIMIT 369;\nINSERT INTO esportsevents SELECT production_usage, bill_id FROM local_impact WHERE production_usage > 474;\nSQL\n", "labels": {"reads": [{"table": "routes", "columns": ["analysis_date", "passenger_count"]}, {"table": "local_impact", "columns": ["production_usage", "bill_id"]}], "writes": [{"table": "esportsevents", "columns": ["production_usage", "bill_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO satellites SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"departments\")\nsrc.write.insertInto(\"contract_transactions\", overwrite=True)\n", "labels": {"reads": [{"table": "departments", "columns": null}], "writes": [{"table": "contract_transactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO sales_2 SELECT actor_name, worker, state, addressid FROM item_prices WHERE actor_name > 132\"\n", "labels": {"reads": [{"table": "item_prices", "columns": ["actor_name", "worker", "state", "addressid"]}], "writes": [{"table": "sales_2", "columns": ["actor_name", "worker", "state", "addressid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO representative SELECT country_name, courses FROM housing_investments WHERE country_name > 383\"\n", "labels": {"reads": [{"table": "housing_investments", "columns": ["country_name", "courses"]}], "writes": [{"table": "representative", "columns": ["country_name", "courses"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO bi.users_df SELECT a.festival_name, b.ihsaa_football_class FROM dispensaries a JOIN ma_inspections b ON a.follow_up_date = b.follow_up_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}, {"table": "ma_inspections", "columns": null}], "writes": [{"table": "bi.users_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT network_name, membership_card FROM water_consumption LIMIT 108\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO exhibitionattendance SELECT grant_date, dish_name FROM donations_insert_2 WHERE grant_date > 388\")\n", "labels": {"reads": [{"table": "water_consumption", "columns": ["network_name", "membership_card"]}, {"table": "donations_insert_2", "columns": ["grant_date", "dish_name"]}], "writes": [{"table": "exhibitionattendance", "columns": ["grant_date", "dish_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"machine\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads\")\n", "labels": {"reads": [{"table": "machine", "columns": null}], "writes": [{"table": "ads", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO menu_items SELECT trip_start_time, established_date, yield, content FROM school_enrollment WHERE trip_start_time > 257;\nEOF\n", "labels": {"reads": [{"table": "school_enrollment", "columns": ["trip_start_time", "established_date", "yield", "content"]}], "writes": [{"table": "menu_items", "columns": ["trip_start_time", "established_date", "yield", "content"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT merchandise_id, unavailable FROM dispensaries LIMIT 324\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "dispensaries", "columns": ["merchandise_id", "unavailable"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO spacecraft_manufacturing SELECT line_name, commanding_officer FROM dws.dws_coupon_use_di WHERE line_name > 335\"], check=True)\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": ["line_name", "commanding_officer"]}], "writes": [{"table": "spacecraft_manufacturing", "columns": ["line_name", "commanding_officer"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"clinics_sa\")\nsrc.write.insertInto(\"contractnegotiations\", overwrite=True)\n", "labels": {"reads": [{"table": "clinics_sa", "columns": null}], "writes": [{"table": "contractnegotiations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO cloud_issues SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.document_description > 329).all()\n# src table: lives_in\nengine.execute(\"INSERT INTO station SELECT * FROM lives_in\")\n", "labels": {"reads": [{"table": "lives_in", "columns": null}], "writes": [{"table": "station", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO indigenouscommunities SELECT a.mealname, b.prod_id FROM dw.users_hourly a JOIN attorney_billing_rates b ON a.location_id = b.location_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": null}, {"table": "attorney_billing_rates", "columns": null}], "writes": [{"table": "indigenouscommunities", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.device_log_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"community_members\")\n", "labels": {"reads": [{"table": "dws.device_log_df", "columns": null}], "writes": [{"table": "community_members", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO droughthistory SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"volunteer_events\")\n", "labels": {"reads": [{"table": "vehicle", "columns": null}], "writes": [{"table": "volunteer_events", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"mart_shipments_full\")\npersist_to_output(df, \"open_pedagogy\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}], "writes": [{"table": "open_pedagogy", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO attendee_demographics SELECT stu_dob, shipping_agent_name, license_type, revenue FROM service_budget WHERE stu_dob > 8\"\n", "labels": {"reads": [{"table": "service_budget", "columns": ["stu_dob", "shipping_agent_name", "license_type", "revenue"]}], "writes": [{"table": "attendee_demographics", "columns": ["stu_dob", "shipping_agent_name", "license_type", "revenue"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT animal_species, phone_number FROM patents LIMIT 99\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "patents", "columns": ["animal_species", "phone_number"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart_refunds SELECT last_year, number_cities, discovery_date, crop_id FROM atlantic_ocean_fish WHERE last_year > 96\"\n", "labels": {"reads": [{"table": "atlantic_ocean_fish", "columns": ["last_year", "number_cities", "discovery_date", "crop_id"]}], "writes": [{"table": "mart_refunds", "columns": ["last_year", "number_cities", "discovery_date", "crop_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"animal_population\")\nexport_to_target(df, \"donationprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "animal_population", "columns": null}], "writes": [{"table": "donationprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO development_hours SELECT * FROM legacy\ncur.execute(\"SELECT claimid, tourist_id FROM candidate_assessments LIMIT 212\")\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": ["claimid", "tourist_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM appointment\", conn)\ndf.to_sql(\"security_incidents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "appointment", "columns": null}], "writes": [{"table": "security_incidents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"healthcare_centers\")\nsrc.write.insertInto(\"landfills\", overwrite=True)\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": null}], "writes": [{"table": "landfills", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO food_production SELECT doctor_id, training_name, contributorid FROM gymc_members WHERE doctor_id > 38\"\n", "labels": {"reads": [{"table": "gymc_members", "columns": ["doctor_id", "training_name", "contributorid"]}], "writes": [{"table": "food_production", "columns": ["doctor_id", "training_name", "contributorid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"veteran_employment\").toPandas()\ndf[[\"branch_id\", \"vol_id\"]].to_sql(\"shipment\", engine, index=False)\n", "labels": {"reads": [{"table": "veteran_employment", "columns": null}], "writes": [{"table": "shipment", "columns": ["branch_id", "vol_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"factory_connections\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"wastewater_facilities\")\n", "labels": {"reads": [{"table": "factory_connections", "columns": null}], "writes": [{"table": "wastewater_facilities", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sustainable_practices_2 SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM skincaresales\"\n", "labels": {"reads": [{"table": "skincaresales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM union_members\", conn)\ndf.to_sql(\"drama_workshop_groups\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "union_members", "columns": null}], "writes": [{"table": "drama_workshop_groups", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"fair_trade_brands\")\npush_to_output(df, \"civilcases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fair_trade_brands", "columns": null}], "writes": [{"table": "civilcases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO convictions SELECT researcher_name, status_code, artworkyear, assessment_date FROM climate_communication WHERE researcher_name > 24\")\n", "labels": {"reads": [{"table": "climate_communication", "columns": ["researcher_name", "status_code", "artworkyear", "assessment_date"]}], "writes": [{"table": "convictions", "columns": ["researcher_name", "status_code", "artworkyear", "assessment_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO heritage_sites_3 SELECT squadron, local_authority, attribute_name FROM tryout WHERE squadron > 385;\nEOF\n", "labels": {"reads": [{"table": "tryout", "columns": ["squadron", "local_authority", "attribute_name"]}], "writes": [{"table": "heritage_sites_3", "columns": ["squadron", "local_authority", "attribute_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO movie SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.participant_type_code > 312).all()\n# src table: transportation\nengine.execute(\"INSERT INTO expenditure SELECT * FROM transportation\")\n", "labels": {"reads": [{"table": "transportation", "columns": null}], "writes": [{"table": "expenditure", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"projectemployees\")\ndump_to_target(df, \"programoutcomes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "projectemployees", "columns": null}], "writes": [{"table": "programoutcomes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO airport_aircraft SELECT file_size, mean_temperature_f, manufacturername FROM zipcodes WHERE file_size > 111\")\n", "labels": {"reads": [{"table": "zipcodes", "columns": ["file_size", "mean_temperature_f", "manufacturername"]}], "writes": [{"table": "airport_aircraft", "columns": ["file_size", "mean_temperature_f", "manufacturername"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"factories\")\nsrc.write.insertInto(\"dwd.dwd_risk_score_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "factories", "columns": null}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO traveler SELECT home_city, famous_title, facultyid, class_section FROM spacecraft_manufacturing WHERE home_city > 411\"\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": ["home_city", "famous_title", "facultyid", "class_section"]}], "writes": [{"table": "traveler", "columns": ["home_city", "famous_title", "facultyid", "class_section"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO lenders (serve_id, response_time) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "lenders", "columns": ["serve_id", "response_time"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donations_insert_2\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_products\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ref_calendar\")\n", "labels": {"reads": [{"table": "bi_products", "columns": null}], "writes": [{"table": "ref_calendar", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO vessel_incident_count SELECT a.vehicle_model, b.heartrate FROM sustainablebrands a JOIN directors b ON a.head_id = b.head_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": null}, {"table": "directors", "columns": null}], "writes": [{"table": "vessel_incident_count", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO org_climate_finance SELECT company_name, ship_name FROM record WHERE company_name > 350;\nEOF\n", "labels": {"reads": [{"table": "record", "columns": ["company_name", "ship_name"]}], "writes": [{"table": "org_climate_finance", "columns": ["company_name", "ship_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"farms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"parks\")\n", "labels": {"reads": [{"table": "farms", "columns": null}], "writes": [{"table": "parks", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO transportation_trips SELECT cost, police_officers FROM researchprojects WHERE cost > 178\"], check=True)\n", "labels": {"reads": [{"table": "researchprojects", "columns": ["cost", "police_officers"]}], "writes": [{"table": "transportation_trips", "columns": ["cost", "police_officers"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd_coupon_use_hourly SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hireid, starting_year FROM vessel_incident_count LIMIT 330;\nINSERT INTO transport SELECT severity, visitor_count, observation_date FROM people_addresses WHERE severity > 180;\nSQL\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": ["hireid", "starting_year"]}, {"table": "people_addresses", "columns": ["severity", "visitor_count", "observation_date"]}], "writes": [{"table": "transport", "columns": ["severity", "visitor_count", "observation_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO shariah_financing SELECT catalog_entry_name, menucategory, borough, count_time FROM ads.vendors_delta WHERE catalog_entry_name > 369;\nEOF\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": ["catalog_entry_name", "menucategory", "borough", "count_time"]}], "writes": [{"table": "shariah_financing", "columns": ["catalog_entry_name", "menucategory", "borough", "count_time"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model nailpolishsales depends on iot_sensors\ndbt build --models nailpolishsales --vars '{\"src\":\"iot_sensors\"}'\n", "labels": {"reads": [{"table": "iot_sensors", "columns": null}], "writes": [{"table": "nailpolishsales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO contracts SELECT heritage_site_id, warehouse_state, startdate FROM higher_ed.publications WHERE heritage_site_id > 246;\nEOF\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": ["heritage_site_id", "warehouse_state", "startdate"]}], "writes": [{"table": "contracts", "columns": ["heritage_site_id", "warehouse_state", "startdate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_events_df SELECT treatment_id, journalist_id, sale_price, meal_id FROM publications WHERE treatment_id > 374\"], check=True)\n", "labels": {"reads": [{"table": "publications", "columns": ["treatment_id", "journalist_id", "sale_price", "meal_id"]}], "writes": [{"table": "bi.bi_events_df", "columns": ["treatment_id", "journalist_id", "sale_price", "meal_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO biosensors.patents SELECT project, mealname, profession_count FROM insurancetype WHERE project > 434\"\n", "labels": {"reads": [{"table": "insurancetype", "columns": ["project", "mealname", "profession_count"]}], "writes": [{"table": "biosensors.patents", "columns": ["project", "mealname", "profession_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT budgeted, professional_development_programs FROM mart_cart_item_di LIMIT 330\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO green_projects SELECT tonnage, location_id, roomname, pilot_name FROM performance_scores WHERE tonnage > 186\")\n", "labels": {"reads": [{"table": "mart_cart_item_di", "columns": ["budgeted", "professional_development_programs"]}, {"table": "performance_scores", "columns": ["tonnage", "location_id", "roomname", "pilot_name"]}], "writes": [{"table": "green_projects", "columns": ["tonnage", "location_id", "roomname", "pilot_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.shipments_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_member_point_daily\")\n", "labels": {"reads": [{"table": "mart.shipments_df", "columns": null}], "writes": [{"table": "ads.ads_member_point_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"voting_record\")\ndump_to_output(df, \"militaryequipment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "voting_record", "columns": null}], "writes": [{"table": "militaryequipment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ota_revenue --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"stg.stg_risk_score_hourly\")\nsave_to_warehouse(df, \"workout\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": null}], "writes": [{"table": "workout", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"farmer_details\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"gamestats\")\n", "labels": {"reads": [{"table": "farmer_details", "columns": null}], "writes": [{"table": "gamestats", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"participants_in_events\")\nsrc.write.insertInto(\"competition\", overwrite=True)\n", "labels": {"reads": [{"table": "participants_in_events", "columns": null}], "writes": [{"table": "competition", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"test_drives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "test_drives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_inventory_di SELECT a.user_login, b.attendee_age FROM brands a JOIN west_providers b ON a.method_id = b.method_id\"\n", "labels": {"reads": [{"table": "brands", "columns": null}, {"table": "west_providers", "columns": null}], "writes": [{"table": "bi.bi_inventory_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT next_maintenance, workers FROM defense_contracts_v2\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"research.species\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": ["next_maintenance", "workers"]}], "writes": [{"table": "research.species", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stops SELECT a.mean_temperature_f, b.community_size FROM ods.ods_exposure_delta a JOIN unesco_intangible_heritage b ON a.attendance_id = b.attendance_id\"\n", "labels": {"reads": [{"table": "ods.ods_exposure_delta", "columns": null}, {"table": "unesco_intangible_heritage", "columns": null}], "writes": [{"table": "stops", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO disaster_zones SELECT date_assigned_from, clinic_type, transaction_type FROM coralreefs WHERE date_assigned_from > 191\"\n", "labels": {"reads": [{"table": "coralreefs", "columns": ["date_assigned_from", "clinic_type", "transaction_type"]}], "writes": [{"table": "disaster_zones", "columns": ["date_assigned_from", "clinic_type", "transaction_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO reservoirs SELECT animal_species, workouttype, time_id, cultural_competency_score FROM has_amenity WHERE animal_species > 340\"\n", "labels": {"reads": [{"table": "has_amenity", "columns": ["animal_species", "workouttype", "time_id", "cultural_competency_score"]}], "writes": [{"table": "reservoirs", "columns": ["animal_species", "workouttype", "time_id", "cultural_competency_score"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"locations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fertilizer\")\n", "labels": {"reads": [{"table": "locations", "columns": null}], "writes": [{"table": "fertilizer", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_types\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mines\")\n", "labels": {"reads": [{"table": "vessel_types", "columns": null}], "writes": [{"table": "mines", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT address_line_1, updatedate FROM militarydrones LIMIT 41\")\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO dws.dws_cart_item_daily SELECT drought_id, year_built FROM tryout WHERE drought_id > 156\")\n", "labels": {"reads": [{"table": "militarydrones", "columns": ["address_line_1", "updatedate"]}, {"table": "tryout", "columns": ["drought_id", "year_built"]}], "writes": [{"table": "dws.dws_cart_item_daily", "columns": ["drought_id", "year_built"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO courtcases SELECT a.how_to_get_there, b.annual_revenue FROM renewable_energy_investments a JOIN bi.bi_sessions_hourly b ON a.unit_of_measure = b.unit_of_measure\"\n", "labels": {"reads": [{"table": "renewable_energy_investments", "columns": null}, {"table": "bi.bi_sessions_hourly", "columns": null}], "writes": [{"table": "courtcases", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT permitid, region_id FROM donationhistory LIMIT 478\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "donationhistory", "columns": ["permitid", "region_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO wellbeing_programs SELECT a.stageposition, b.framework_id FROM international_visitors a JOIN ods_payments_delta b ON a.first_name = b.first_name\"\n", "labels": {"reads": [{"table": "international_visitors", "columns": null}, {"table": "ods_payments_delta", "columns": null}], "writes": [{"table": "wellbeing_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO lead_mines SELECT attendee_id, hotel_name, destination_name FROM submersible_dives WHERE attendee_id > 48\"\n", "labels": {"reads": [{"table": "submersible_dives", "columns": ["attendee_id", "hotel_name", "destination_name"]}], "writes": [{"table": "lead_mines", "columns": ["attendee_id", "hotel_name", "destination_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_mammals\").toPandas()\ndf[[\"projecttype\", \"mental_health_rating\"]].to_sql(\"financialwellbeing\", engine, index=False)\n", "labels": {"reads": [{"table": "marine_mammals", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": ["projecttype", "mental_health_rating"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT therapeutic_area, supplier_country FROM phishing_attempts LIMIT 174;\nINSERT INTO recycling_stats SELECT sportname, mediatypeid FROM heritage_sites_3 WHERE sportname > 370;\nSQL\n", "labels": {"reads": [{"table": "phishing_attempts", "columns": ["therapeutic_area", "supplier_country"]}, {"table": "heritage_sites_3", "columns": ["sportname", "mediatypeid"]}], "writes": [{"table": "recycling_stats", "columns": ["sportname", "mediatypeid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attack_outcomes\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "attack_outcomes", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO levees (treatment_type, date_stored) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "levees", "columns": ["treatment_type", "date_stored"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"epl_teams\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"org_donation\")\n", "labels": {"reads": [{"table": "epl_teams", "columns": null}], "writes": [{"table": "org_donation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT requestid, voter_id FROM bi.bi_events_df LIMIT 401\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "bi.bi_events_df", "columns": ["requestid", "voter_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT field_name, emp_fname FROM dwd.dwd_orders_daily\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"episodes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": ["field_name", "emp_fname"]}], "writes": [{"table": "episodes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.stg_campaigns_hourly (name_last, assigned_to_staff_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_campaigns_hourly", "columns": ["name_last", "assigned_to_staff_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT amount_outstanding, area_ha FROM co2_sequestration LIMIT 179;\nINSERT INTO tickets SELECT policytype, eliminated_by, region, contract_start_date FROM renewableprojects WHERE policytype > 78;\nSQL\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": ["amount_outstanding", "area_ha"]}, {"table": "renewableprojects", "columns": ["policytype", "eliminated_by", "region", "contract_start_date"]}], "writes": [{"table": "tickets", "columns": ["policytype", "eliminated_by", "region", "contract_start_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO carbon_offset_projects SELECT a.max_salary, b.open_date FROM waterconservation a JOIN user_ad_interactions b ON a.job_title_code = b.job_title_code\"\n", "labels": {"reads": [{"table": "waterconservation", "columns": null}, {"table": "user_ad_interactions", "columns": null}], "writes": [{"table": "carbon_offset_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT capacity_percentage, capacity FROM bi.device_log_hourly LIMIT 431\")\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO exit_strategy SELECT functional_area_description, style FROM community_education_programs WHERE functional_area_description > 292\")\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": ["capacity_percentage", "capacity"]}, {"table": "community_education_programs", "columns": ["functional_area_description", "style"]}], "writes": [{"table": "exit_strategy", "columns": ["functional_area_description", "style"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT cargo_weight, teamname FROM loan LIMIT 328;\nINSERT INTO habitat_preservation SELECT pallet_id, water_depth, fan_name FROM department_publications WHERE pallet_id > 322;\nSQL\n", "labels": {"reads": [{"table": "loan", "columns": ["cargo_weight", "teamname"]}, {"table": "department_publications", "columns": ["pallet_id", "water_depth", "fan_name"]}], "writes": [{"table": "habitat_preservation", "columns": ["pallet_id", "water_depth", "fan_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.target_id > 169).all()\n# src table: dwd.coupon_use_daily\nengine.execute(\"INSERT INTO thefttypes SELECT * FROM dwd.coupon_use_daily\")\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": null}], "writes": [{"table": "thefttypes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO humanitarian_assistance SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT genrename, username FROM stars\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"employees\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stars", "columns": ["genrename", "username"]}], "writes": [{"table": "employees", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT container_count, emp_dob FROM smart_contracts LIMIT 186;\nINSERT INTO ads.ads_inventory_df SELECT saleamount, countryid, date_order_placed, rank_in_round FROM whale_sharks WHERE saleamount > 435;\nSQL\n", "labels": {"reads": [{"table": "smart_contracts", "columns": ["container_count", "emp_dob"]}, {"table": "whale_sharks", "columns": ["saleamount", "countryid", "date_order_placed", "rank_in_round"]}], "writes": [{"table": "ads.ads_inventory_df", "columns": ["saleamount", "countryid", "date_order_placed", "rank_in_round"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"iot_sensors\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "iot_sensors", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"temperature_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "temperature_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM org_donation\"\n", "labels": {"reads": [{"table": "org_donation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO ref_service_types SELECT a.style, b.involved_in_lifelong_learning FROM stg.refunds_hourly a JOIN products_in_events b ON a.albumname = b.albumname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.refunds_hourly", "columns": null}, {"table": "products_in_events", "columns": null}], "writes": [{"table": "ref_service_types", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"stg.campaigns_df\")\nwrite_to_store(df, \"tech_for_social_good\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": null}], "writes": [{"table": "tech_for_social_good", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.department_id > 193).all()\n# src table: ap_budget\nengine.execute(\"INSERT INTO researchpapers SELECT * FROM ap_budget\")\n", "labels": {"reads": [{"table": "ap_budget", "columns": null}], "writes": [{"table": "researchpapers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO payments (categoryname, daily_sales) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "payments", "columns": ["categoryname", "daily_sales"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ai_adoption_date, primaryaffiliation FROM police_officers_tx LIMIT 307\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "police_officers_tx", "columns": ["ai_adoption_date", "primaryaffiliation"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"discount_coupons\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"customer_addresses\")\n", "labels": {"reads": [{"table": "discount_coupons", "columns": null}], "writes": [{"table": "customer_addresses", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO mart.mart_users SELECT openingid, language_id FROM agri_innovations WHERE openingid > 284\")\n", "labels": {"reads": [{"table": "agri_innovations", "columns": ["openingid", "language_id"]}], "writes": [{"table": "mart.mart_users", "columns": ["openingid", "language_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM events\", conn)\ndf.to_sql(\"ods_clicks_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "events", "columns": null}], "writes": [{"table": "ods_clicks_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_stored, stock FROM medicine LIMIT 300\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "medicine", "columns": ["date_stored", "stock"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mart.mart_users_di\")\nsrc.write.insertInto(\"aquatic_farms\", overwrite=True)\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": null}], "writes": [{"table": "aquatic_farms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.feedid > 441).all()\n# src table: teacher_professional_development\nengine.execute(\"INSERT INTO org_donation SELECT * FROM teacher_professional_development\")\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": null}], "writes": [{"table": "org_donation", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws.payments_delta\", conn)\ndf.to_sql(\"material_production\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": null}], "writes": [{"table": "material_production", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT college_id, party FROM threat_intelligence LIMIT 374\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO productsafety SELECT num_of_staff, discovered_date FROM animal_species WHERE num_of_staff > 477\")\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["college_id", "party"]}, {"table": "animal_species", "columns": ["num_of_staff", "discovered_date"]}], "writes": [{"table": "productsafety", "columns": ["num_of_staff", "discovered_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.dws_events_df (comment_count, train_number) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_events_df", "columns": ["comment_count", "train_number"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attorneyid, friend FROM route\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"disinformation_detection\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "route", "columns": ["attorneyid", "friend"]}], "writes": [{"table": "disinformation_detection", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table posts_per_day --columns emp_num,policyname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "posts_per_day", "columns": ["emp_num", "policyname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"casebilling\").toPandas()\ndf[[\"draft_class\", \"policyholderid\"]].to_sql(\"dws.dws_inventory_di\", engine, index=False)\n", "labels": {"reads": [{"table": "casebilling", "columns": null}], "writes": [{"table": "dws.dws_inventory_di", "columns": ["draft_class", "policyholderid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO permit SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artistsdemographics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"paris_train\")\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": null}], "writes": [{"table": "paris_train", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table affordablehousing --columns hourdate,operation_count --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "affordablehousing", "columns": ["hourdate", "operation_count"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO course SELECT a.route_short_name, b.segment_id FROM donationsbycause a JOIN restaurant b ON a.is_dessert = b.is_dessert\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "donationsbycause", "columns": null}, {"table": "restaurant", "columns": null}], "writes": [{"table": "course", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"section\")\nsrc.write.insertInto(\"model_fairness\", overwrite=True)\n", "labels": {"reads": [{"table": "section", "columns": null}], "writes": [{"table": "model_fairness", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO athletes_performance SELECT num_virtual_tours, membership_card, productivity FROM dws.dws_orders_full WHERE num_virtual_tours > 147;\nEOF\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": ["num_virtual_tours", "membership_card", "productivity"]}], "writes": [{"table": "athletes_performance", "columns": ["num_virtual_tours", "membership_card", "productivity"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.risk_score_df\", conn)\ndf.to_sql(\"traditional_arts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.risk_score_df", "columns": null}], "writes": [{"table": "traditional_arts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM peacekeepingmissions\"\n", "labels": {"reads": [{"table": "peacekeepingmissions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rainfall_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rainfall_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO mart.mart_refunds_di SELECT classroom, patient_count FROM dws.dws_refunds_daily WHERE classroom > 155\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_daily", "columns": ["classroom", "patient_count"]}], "writes": [{"table": "mart.mart_refunds_di", "columns": ["classroom", "patient_count"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO carbon_offset_south_america (amenid, fine) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "carbon_offset_south_america", "columns": ["amenid", "fine"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO teachers SELECT a.innovation, b.membership_card FROM production a JOIN dws.dws_refunds_hourly b ON a.event_id = b.event_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "production", "columns": null}, {"table": "dws.dws_refunds_hourly", "columns": null}], "writes": [{"table": "teachers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.materialtype > 49).all()\n# src table: school_roster\nengine.execute(\"INSERT INTO visitor_statistics SELECT * FROM school_roster\")\n", "labels": {"reads": [{"table": "school_roster", "columns": null}], "writes": [{"table": "visitor_statistics", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO wellbeing_programs SELECT warehouseid, strategy_name, amount_outstanding, voter_id FROM store WHERE warehouseid > 5\"\n", "labels": {"reads": [{"table": "store", "columns": ["warehouseid", "strategy_name", "amount_outstanding", "voter_id"]}], "writes": [{"table": "wellbeing_programs", "columns": ["warehouseid", "strategy_name", "amount_outstanding", "voter_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_certification\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "green_certification", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO africa_projects SELECT num_workers, manager_id, category_name FROM timber_sales WHERE num_workers > 237\"\n", "labels": {"reads": [{"table": "timber_sales", "columns": ["num_workers", "manager_id", "category_name"]}], "writes": [{"table": "africa_projects", "columns": ["num_workers", "manager_id", "category_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO virtual_tour_offers (completed_course, booked_amount) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "virtual_tour_offers", "columns": ["completed_course", "booked_amount"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT environmental_impact, product_price FROM carbon_offsets\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"invoices\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["environmental_impact", "product_price"]}], "writes": [{"table": "invoices", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM habitat3\", conn)\ndf.to_sql(\"players\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "habitat3", "columns": null}], "writes": [{"table": "players", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.energy_efficiency_rating > 110).all()\n# src table: immunizationrates\nengine.execute(\"INSERT INTO climate_data SELECT * FROM immunizationrates\")\n", "labels": {"reads": [{"table": "immunizationrates", "columns": null}], "writes": [{"table": "climate_data", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO regulatory_compliance SELECT product_price, cost FROM founder WHERE product_price > 29\"\n", "labels": {"reads": [{"table": "founder", "columns": ["product_price", "cost"]}], "writes": [{"table": "regulatory_compliance", "columns": ["product_price", "cost"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dwd.users_daily SELECT pressure, observation_date FROM organizations WHERE pressure > 255\"\n", "labels": {"reads": [{"table": "organizations", "columns": ["pressure", "observation_date"]}], "writes": [{"table": "dwd.users_daily", "columns": ["pressure", "observation_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT crime_type, grant_amount FROM armed_forces\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"dws.payments_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "armed_forces", "columns": ["crime_type", "grant_amount"]}], "writes": [{"table": "dws.payments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"biotech.startups\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "biotech.startups", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table healthcare_budget --target-dir /tmp/land\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mining_operation\", conn)\ndf.to_sql(\"freightforwarding\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mining_operation", "columns": null}], "writes": [{"table": "freightforwarding", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO labor_unions SELECT diet, quality_rank, color_code, yearid FROM workforce_development WHERE diet > 343\"\n", "labels": {"reads": [{"table": "workforce_development", "columns": ["diet", "quality_rank", "color_code", "yearid"]}], "writes": [{"table": "labor_unions", "columns": ["diet", "quality_rank", "color_code", "yearid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT transaction_amount, platform FROM legalaidrequests LIMIT 458\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": ["transaction_amount", "platform"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table school_roster --columns calories,volume_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "school_roster", "columns": ["calories", "volume_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"expensive_space_missions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"recalls\")\n", "labels": {"reads": [{"table": "expensive_space_missions", "columns": null}], "writes": [{"table": "recalls", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dependent SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_distribution\").toPandas()\ndf[[\"ethical_certifications\", \"lastdonationdate\"]].to_sql(\"multimodal_trips\", engine, index=False)\n", "labels": {"reads": [{"table": "water_distribution", "columns": null}], "writes": [{"table": "multimodal_trips", "columns": ["ethical_certifications", "lastdonationdate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.shipments_full (company, taxi_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.shipments_full", "columns": ["company", "taxi_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO invoices SELECT dishname, goldid, store_id FROM workshops WHERE dishname > 137\"\n", "labels": {"reads": [{"table": "workshops", "columns": ["dishname", "goldid", "store_id"]}], "writes": [{"table": "invoices", "columns": ["dishname", "goldid", "store_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_in_locaton_to, animal_type FROM waterconservation\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"faculty\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "waterconservation", "columns": ["date_in_locaton_to", "animal_type"]}], "writes": [{"table": "faculty", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO perpetrator SELECT therapy_sessions, is_eco_friendly FROM publications WHERE therapy_sessions > 275\")\n", "labels": {"reads": [{"table": "publications", "columns": ["therapy_sessions", "is_eco_friendly"]}], "writes": [{"table": "perpetrator", "columns": ["therapy_sessions", "is_eco_friendly"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO satellites_in_orbit SELECT transaction_type_description, spending FROM skincareproducts WHERE transaction_type_description > 329\"\n", "labels": {"reads": [{"table": "skincareproducts", "columns": ["transaction_type_description", "spending"]}], "writes": [{"table": "satellites_in_orbit", "columns": ["transaction_type_description", "spending"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart.mart_shipments_hourly\"\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO veteran_occupations SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO region SELECT a.cargo_id, b.donorname FROM facility a JOIN vehicle_prices b ON a.building_manager = b.building_manager\"\n", "labels": {"reads": [{"table": "facility", "columns": null}, {"table": "vehicle_prices", "columns": null}], "writes": [{"table": "region", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table volunteer_hours --target-dir /tmp/land\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO volunteerhours SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT passenger_id, reviewscore FROM landfill_capacity\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"military_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": ["passenger_id", "reviewscore"]}], "writes": [{"table": "military_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mars_missions SELECT area, account_name, contract_start FROM reverselogisticstransactions WHERE area > 265\"\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": ["area", "account_name", "contract_start"]}], "writes": [{"table": "mars_missions", "columns": ["area", "account_name", "contract_start"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO landfill_capacity (born_state, total_spent) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "landfill_capacity", "columns": ["born_state", "total_spent"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO online_platform SELECT * FROM legacy\ncur.execute(\"SELECT causename, airport_name FROM militaryequipment LIMIT 430\")\n", "labels": {"reads": [{"table": "militaryequipment", "columns": ["causename", "airport_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.orders_daily SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table member_attendance --columns log_entry_date,strainname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "member_attendance", "columns": ["log_entry_date", "strainname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mobile_customers_global (circular_supply_chain, bats) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mobile_customers_global", "columns": ["circular_supply_chain", "bats"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM distributors\", conn)\ndf.to_sql(\"prescribes\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "distributors", "columns": null}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"staff_members\")\nsrc.write.insertInto(\"social_impact_bonds\", overwrite=True)\n", "labels": {"reads": [{"table": "staff_members", "columns": null}], "writes": [{"table": "social_impact_bonds", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model sites depends on fare_segments\ndbt build --models sites --vars 'source: fare_segments'\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": [{"table": "sites", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO green_buildings SELECT case_id, excavationid, ticket_id, surface_area FROM mammals WHERE case_id > 24\"\n", "labels": {"reads": [{"table": "mammals", "columns": ["case_id", "excavationid", "ticket_id", "surface_area"]}], "writes": [{"table": "green_buildings", "columns": ["case_id", "excavationid", "ticket_id", "surface_area"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_data\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"policy_feedback\")\n", "labels": {"reads": [{"table": "safety_data", "columns": null}], "writes": [{"table": "policy_feedback", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO seeds SELECT year_founded, production_date FROM product_review WHERE year_founded > 42\"\n", "labels": {"reads": [{"table": "product_review", "columns": ["year_founded", "production_date"]}], "writes": [{"table": "seeds", "columns": ["year_founded", "production_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO nutrition_facts SELECT * FROM legacy\ncur.execute(\"SELECT center, gymnast_id FROM temperature LIMIT 115\")\n", "labels": {"reads": [{"table": "temperature", "columns": ["center", "gymnast_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO certificate SELECT * FROM legacy\ncur.execute(\"SELECT snatch, donor_category FROM tb_reports LIMIT 229\")\n", "labels": {"reads": [{"table": "tb_reports", "columns": ["snatch", "donor_category"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table dw.clicks_di --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw.clicks_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacemissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.dwd_payments_di\")\n", "labels": {"reads": [{"table": "spacemissions", "columns": null}], "writes": [{"table": "dwd.dwd_payments_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO shipment (employmentdate, citizens) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "shipment", "columns": ["employmentdate", "citizens"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT detention_type_code, built_year FROM virtual_tourism\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"subscribers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["detention_type_code", "built_year"]}], "writes": [{"table": "subscribers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model birds depends on architect\ndbt run --models birds --vars 'source: architect'\n", "labels": {"reads": [{"table": "architect", "columns": null}], "writes": [{"table": "birds", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model road_construction depends on tencel_sources\ndbt run --select road_construction --vars 'source: tencel_sources'\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}], "writes": [{"table": "road_construction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM investment_accounts\"\n", "labels": {"reads": [{"table": "investment_accounts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table public.crime_types --columns delegate,draft_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "public.crime_types", "columns": ["delegate", "draft_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM maintenancerequests\", conn)\ndf.to_sql(\"dw.inventory_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": null}], "writes": [{"table": "dw.inventory_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"candidate_assessments\").toPandas()\ndf[[\"indigenous\", \"restaurant_id\"]].to_sql(\"climate_adaptation_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}], "writes": [{"table": "climate_adaptation_projects", "columns": ["indigenous", "restaurant_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO animal_population SELECT a.last_updated, b.unavailable FROM movies a JOIN container_ships b ON a.artifact_weight = b.artifact_weight\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "movies", "columns": null}, {"table": "container_ships", "columns": null}], "writes": [{"table": "animal_population", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO retail_workers_union SELECT safety_record, investment_id, num_developments, goals FROM factories WHERE safety_record > 98\"\n", "labels": {"reads": [{"table": "factories", "columns": ["safety_record", "investment_id", "num_developments", "goals"]}], "writes": [{"table": "retail_workers_union", "columns": ["safety_record", "investment_id", "num_developments", "goals"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO bridge SELECT test_result, gametype FROM visual_arts WHERE test_result > 404\"\n", "labels": {"reads": [{"table": "visual_arts", "columns": ["test_result", "gametype"]}], "writes": [{"table": "bridge", "columns": ["test_result", "gametype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table smart_grids --columns individual_last_name,views --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "smart_grids", "columns": ["individual_last_name", "views"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"contracts\").toPandas()\ndf[[\"mhw_id\", \"constructorid\"]].to_sql(\"fleets\", engine, index=False)\n", "labels": {"reads": [{"table": "contracts", "columns": null}], "writes": [{"table": "fleets", "columns": ["mhw_id", "constructorid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO furniture SELECT a.budget_amount, b.hometeam FROM upgrades a JOIN traditional_arts b ON a.height_feet = b.height_feet\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "upgrades", "columns": null}, {"table": "traditional_arts", "columns": null}], "writes": [{"table": "furniture", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"employee\")\nsrc.write.insertInto(\"garments\", overwrite=True)\n", "labels": {"reads": [{"table": "employee", "columns": null}], "writes": [{"table": "garments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"protein\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "protein", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO city_tech SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"financial_capability\")\nsink_to_output(df, \"elimination\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "financial_capability", "columns": null}], "writes": [{"table": "elimination", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO malicious_activity SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.dwd_member_point_full --columns employee,crime_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_member_point_full", "columns": ["employee", "crime_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"festivals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bike_station_info\")\n", "labels": {"reads": [{"table": "festivals", "columns": null}], "writes": [{"table": "bike_station_info", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table performance_scores --target-dir /tmp/land\n", "labels": {"reads": [{"table": "performance_scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.coupon_use (fault_description, creation_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.coupon_use", "columns": ["fault_description", "creation_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO waste SELECT permit_number, is_organic FROM artsales WHERE permit_number > 101\"], check=True)\n", "labels": {"reads": [{"table": "artsales", "columns": ["permit_number", "is_organic"]}], "writes": [{"table": "waste", "columns": ["permit_number", "is_organic"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT copy_number, brand_name FROM technician LIMIT 78;\nINSERT INTO environmentalimpact SELECT productid, case_outcome, iata FROM therapy_session WHERE productid > 216;\nSQL\n", "labels": {"reads": [{"table": "technician", "columns": ["copy_number", "brand_name"]}, {"table": "therapy_session", "columns": ["productid", "case_outcome", "iata"]}], "writes": [{"table": "environmentalimpact", "columns": ["productid", "case_outcome", "iata"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacecraft\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ods.ods_member_point_df\")\n", "labels": {"reads": [{"table": "spacecraft", "columns": null}], "writes": [{"table": "ods.ods_member_point_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO recyclers (fare_id, forest_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "recyclers", "columns": ["fare_id", "forest_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT functional_area_code, project_category FROM economic_diversification_projects LIMIT 77;\nINSERT INTO haircare_cruelty SELECT cuisine_name, distributorid, organisation_type_description, drug FROM military_personnel_africa WHERE cuisine_name > 30;\nSQL\n", "labels": {"reads": [{"table": "economic_diversification_projects", "columns": ["functional_area_code", "project_category"]}, {"table": "military_personnel_africa", "columns": ["cuisine_name", "distributorid", "organisation_type_description", "drug"]}], "writes": [{"table": "haircare_cruelty", "columns": ["cuisine_name", "distributorid", "organisation_type_description", "drug"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table soccer_goals --columns totalprice,num_developments --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "soccer_goals", "columns": ["totalprice", "num_developments"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT mh_id, document_code FROM policy_feedback\", engine)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"attack_outcomes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "policy_feedback", "columns": ["mh_id", "document_code"]}], "writes": [{"table": "attack_outcomes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO european_healthcare SELECT a.catalog_level_number, b.arrival_date FROM bi.bi_orders_delta a JOIN cloud_issues b ON a.awayteamid = b.awayteamid\"\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": null}, {"table": "cloud_issues", "columns": null}], "writes": [{"table": "european_healthcare", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO student_courses SELECT a.productiondate, b.truck_licence_number FROM gamereviews a JOIN election b ON a.casestatus = b.casestatus\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "gamereviews", "columns": null}, {"table": "election", "columns": null}], "writes": [{"table": "student_courses", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO workouts SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO program_funding_2 SELECT led_by, department FROM public.ev_sales WHERE led_by > 218;\nEOF\n", "labels": {"reads": [{"table": "public.ev_sales", "columns": ["led_by", "department"]}], "writes": [{"table": "program_funding_2", "columns": ["led_by", "department"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT driller, quantity_sold FROM africa_schema.african_mines LIMIT 389\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["driller", "quantity_sold"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_refunds\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.stg_inventory_full\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": null}], "writes": [{"table": "stg.stg_inventory_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO festivals SELECT student_capacity, unique_founders, character FROM forms WHERE student_capacity > 176\"\n", "labels": {"reads": [{"table": "forms", "columns": ["student_capacity", "unique_founders", "character"]}], "writes": [{"table": "festivals", "columns": ["student_capacity", "unique_founders", "character"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT vaccine_type, water_type FROM ods.ods_users_daily LIMIT 246;\nINSERT INTO assessment_notes SELECT governor, patient_count FROM incident_region WHERE governor > 207;\nSQL\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": ["vaccine_type", "water_type"]}, {"table": "incident_region", "columns": ["governor", "patient_count"]}], "writes": [{"table": "assessment_notes", "columns": ["governor", "patient_count"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM production_yearly\", conn)\ndf.to_sql(\"employeedemographics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "production_yearly", "columns": null}], "writes": [{"table": "employeedemographics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"communityhealthworkerscanada\")\nsrc.write.insertInto(\"defense_spending\", overwrite=True)\n", "labels": {"reads": [{"table": "communityhealthworkerscanada", "columns": null}], "writes": [{"table": "defense_spending", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_points > 129).all()\n# src table: rural_infrastructure\nengine.execute(\"INSERT INTO dw_users_full SELECT * FROM rural_infrastructure\")\n", "labels": {"reads": [{"table": "rural_infrastructure", "columns": null}], "writes": [{"table": "dw_users_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO aircraft SELECT therapy_date, cmi_details, paper_id, partner_id FROM cuisine WHERE therapy_date > 237\")\n", "labels": {"reads": [{"table": "cuisine", "columns": ["therapy_date", "cmi_details", "paper_id", "partner_id"]}], "writes": [{"table": "aircraft", "columns": ["therapy_date", "cmi_details", "paper_id", "partner_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO community_programs SELECT client, primary_advisor, student_capacity FROM student_course_registrations WHERE client > 96;\nEOF\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": ["client", "primary_advisor", "student_capacity"]}], "writes": [{"table": "community_programs", "columns": ["client", "primary_advisor", "student_capacity"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO defense_contractors SELECT chemical_name, sessionid FROM pacific_ocean WHERE chemical_name > 25\"\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": ["chemical_name", "sessionid"]}], "writes": [{"table": "defense_contractors", "columns": ["chemical_name", "sessionid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT age_group_id, caloric_content FROM policyanalysis\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"ads.orders_daily\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "policyanalysis", "columns": ["age_group_id", "caloric_content"]}], "writes": [{"table": "ads.orders_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO fair_wages (census_ranking, trade_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "fair_wages", "columns": ["census_ranking", "trade_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO strategies SELECT observation_id, customer_number, task_details FROM customersregion WHERE observation_id > 495\"\n", "labels": {"reads": [{"table": "customersregion", "columns": ["observation_id", "customer_number", "task_details"]}], "writes": [{"table": "strategies", "columns": ["observation_id", "customer_number", "task_details"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO artistsales SELECT number_of_vessels, dapp_name FROM tourism_centers WHERE number_of_vessels > 427;\nEOF\n", "labels": {"reads": [{"table": "tourism_centers", "columns": ["number_of_vessels", "dapp_name"]}], "writes": [{"table": "artistsales", "columns": ["number_of_vessels", "dapp_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO nomination (movement, program_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "nomination", "columns": ["movement", "program_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stock --columns minesite,theme --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stock", "columns": ["minesite", "theme"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO maintenancerequests SELECT rank_in_round, interest_group, volume FROM crop_temperature WHERE rank_in_round > 25\"\n", "labels": {"reads": [{"table": "crop_temperature", "columns": ["rank_in_round", "interest_group", "volume"]}], "writes": [{"table": "maintenancerequests", "columns": ["rank_in_round", "interest_group", "volume"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mentalhealthparityviolations SELECT facultyid, student FROM aircraft WHERE facultyid > 57\"\n", "labels": {"reads": [{"table": "aircraft", "columns": ["facultyid", "student"]}], "writes": [{"table": "mentalhealthparityviolations", "columns": ["facultyid", "student"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO consumer_preference SELECT a.year_working, b.neighborhood FROM workers a JOIN courts b ON a.event_details = b.event_details\"\n", "labels": {"reads": [{"table": "workers", "columns": null}, {"table": "courts", "columns": null}], "writes": [{"table": "consumer_preference", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT video_id, network FROM org_volunteer\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"school_bus\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "org_volunteer", "columns": ["video_id", "network"]}], "writes": [{"table": "school_bus", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO farmer_details SELECT clientid, financially_capable FROM student_program_mapping WHERE clientid > 333\"\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": ["clientid", "financially_capable"]}], "writes": [{"table": "farmer_details", "columns": ["clientid", "financially_capable"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_equipment_maintenance\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart_refunds\")\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": null}], "writes": [{"table": "mart_refunds", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO projectemployees SELECT subscribe_date, principal_activities, studio, first_name FROM equipment_maintenance WHERE subscribe_date > 480\"\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": ["subscribe_date", "principal_activities", "studio", "first_name"]}], "writes": [{"table": "projectemployees", "columns": ["subscribe_date", "principal_activities", "studio", "first_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_species_arctic_ocean\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "marine_species_arctic_ocean", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM defense_contractors\"\n", "labels": {"reads": [{"table": "defense_contractors", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO party_forms (num_sessions, asset_disposed_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "party_forms", "columns": ["num_sessions", "asset_disposed_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO military_personnel_africa (address_line_2, carbon_offset_tons) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "military_personnel_africa", "columns": ["address_line_2", "carbon_offset_tons"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO transaction SELECT outcome, asset_make, document_date, decor FROM skincareinventory WHERE outcome > 56\"\n", "labels": {"reads": [{"table": "skincareinventory", "columns": ["outcome", "asset_make", "document_date", "decor"]}], "writes": [{"table": "transaction", "columns": ["outcome", "asset_make", "document_date", "decor"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model machine depends on traffic_violations\ndbt build -s machine --vars '{\"src\":\"traffic_violations\"}'\n", "labels": {"reads": [{"table": "traffic_violations", "columns": null}], "writes": [{"table": "machine", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"royal_family\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.stg_risk_score\")\n", "labels": {"reads": [{"table": "royal_family", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_shipments\", conn)\ndf.to_sql(\"satellite_missions_large\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": null}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cargo_tracking --columns account_type,collection_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cargo_tracking", "columns": ["account_type", "collection_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT shipmentid, zone_name FROM train_station LIMIT 471\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO market_access SELECT service_id, authid FROM trees WHERE service_id > 398\")\n", "labels": {"reads": [{"table": "train_station", "columns": ["shipmentid", "zone_name"]}, {"table": "trees", "columns": ["service_id", "authid"]}], "writes": [{"table": "market_access", "columns": ["service_id", "authid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT customer_details, num_tools FROM membership_register_branch LIMIT 229;\nINSERT INTO baseball_teams SELECT years_played, customer_code, treatment_id, updatedate FROM trust WHERE years_played > 124;\nSQL\n", "labels": {"reads": [{"table": "membership_register_branch", "columns": ["customer_details", "num_tools"]}, {"table": "trust", "columns": ["years_played", "customer_code", "treatment_id", "updatedate"]}], "writes": [{"table": "baseball_teams", "columns": ["years_played", "customer_code", "treatment_id", "updatedate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contractid > 413).all()\n# src table: gamegenres\nengine.execute(\"INSERT INTO marine_life_populations SELECT * FROM gamegenres\")\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": [{"table": "marine_life_populations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT playtime, blockcode FROM team LIMIT 219\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "team", "columns": ["playtime", "blockcode"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_payments_df SELECT a.jobcategory, b.fuelconsumed FROM student_addresses a JOIN economic_diversification_efforts b ON a.intervention_type = b.intervention_type\"\n", "labels": {"reads": [{"table": "student_addresses", "columns": null}, {"table": "economic_diversification_efforts", "columns": null}], "writes": [{"table": "bi.bi_payments_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tracks (haslegalprecedent, vrdevice) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tracks", "columns": ["haslegalprecedent", "vrdevice"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads_users_hourly SELECT a.restaurant_id, b.state_id FROM fleet_management a JOIN network_infrastructure b ON a.focal_length_mm = b.focal_length_mm\"\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}, {"table": "network_infrastructure", "columns": null}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO startup_founders SELECT cost_id, black, founder_group FROM jupiter_spacecraft WHERE cost_id > 5;\nEOF\n", "labels": {"reads": [{"table": "jupiter_spacecraft", "columns": ["cost_id", "black", "founder_group"]}], "writes": [{"table": "startup_founders", "columns": ["cost_id", "black", "founder_group"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM underwater_cables\", conn)\ndf.to_sql(\"ocean_pollution\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "underwater_cables", "columns": null}], "writes": [{"table": "ocean_pollution", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO video_content SELECT a.theftdate, b.winning_aircraft FROM projecttimeline a JOIN endowment b ON a.student_id = b.student_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "projecttimeline", "columns": null}, {"table": "endowment", "columns": null}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"nyc_subway\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"public_participation\")\n", "labels": {"reads": [{"table": "nyc_subway", "columns": null}], "writes": [{"table": "public_participation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO district_schools SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO therapy_session (advocate_name, num_of_audience) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "therapy_session", "columns": ["advocate_name", "num_of_audience"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table emergencies --columns subject_area_id,playerregion --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "emergencies", "columns": ["subject_area_id", "playerregion"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT dno, date_in_location_from FROM student LIMIT 380;\nINSERT INTO sportsinfo SELECT episode_number, peakhourid, customer_name FROM geologicalsurvey WHERE episode_number > 183;\nSQL\n", "labels": {"reads": [{"table": "student", "columns": ["dno", "date_in_location_from"]}, {"table": "geologicalsurvey", "columns": ["episode_number", "peakhourid", "customer_name"]}], "writes": [{"table": "sportsinfo", "columns": ["episode_number", "peakhourid", "customer_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT satelliteid, health_equity_metric_1 FROM dw.dw_events_di LIMIT 176\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": ["satelliteid", "health_equity_metric_1"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw_vendors_di SELECT * FROM legacy\ncur.execute(\"SELECT total_donation_amount, community_id FROM recyclingcenters LIMIT 169\")\n", "labels": {"reads": [{"table": "recyclingcenters", "columns": ["total_donation_amount", "community_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT incident_id, classtype FROM eco_materials LIMIT 368;\nINSERT INTO battery_projects SELECT game_name, projectid, dateundergoes FROM carbon_emissions WHERE game_name > 427;\nSQL\n", "labels": {"reads": [{"table": "eco_materials", "columns": ["incident_id", "classtype"]}, {"table": "carbon_emissions", "columns": ["game_name", "projectid", "dateundergoes"]}], "writes": [{"table": "battery_projects", "columns": ["game_name", "projectid", "dateundergoes"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"gamesessions\")\nwrite_to_target(df, \"waterconservationbudget\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO organization SELECT media_outlet, stationid, asset_acquired_date, vaccination_status FROM bi_products WHERE media_outlet > 326\"], check=True)\n", "labels": {"reads": [{"table": "bi_products", "columns": ["media_outlet", "stationid", "asset_acquired_date", "vaccination_status"]}], "writes": [{"table": "organization", "columns": ["media_outlet", "stationid", "asset_acquired_date", "vaccination_status"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO pollutionincidents SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT avg_usage, vesselid FROM prepaid_mobile\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"beauty_products\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "prepaid_mobile", "columns": ["avg_usage", "vesselid"]}], "writes": [{"table": "beauty_products", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO songs SELECT inclusive_housing_policy, enrollment_date, average FROM sustainable_practices WHERE inclusive_housing_policy > 484\"\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": ["inclusive_housing_policy", "enrollment_date", "average"]}], "writes": [{"table": "songs", "columns": ["inclusive_housing_policy", "enrollment_date", "average"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table training --target-dir /tmp/land\n", "labels": {"reads": [{"table": "training", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cinema SELECT claim_type, stockid, survey_id FROM ports WHERE claim_type > 92\"\n", "labels": {"reads": [{"table": "ports", "columns": ["claim_type", "stockid", "survey_id"]}], "writes": [{"table": "cinema", "columns": ["claim_type", "stockid", "survey_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.openning_year > 103).all()\n# src table: bi.events_delta\nengine.execute(\"INSERT INTO wastewatertreatment SELECT * FROM bi.events_delta\")\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": null}], "writes": [{"table": "wastewatertreatment", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO operate_company (hearingdate, stu_fname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "operate_company", "columns": ["hearingdate", "stu_fname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO pharmasales SELECT pet_age, evaluated_for_fairness FROM tourism_activities WHERE pet_age > 485\"\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["pet_age", "evaluated_for_fairness"]}], "writes": [{"table": "pharmasales", "columns": ["pet_age", "evaluated_for_fairness"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO recycling_stats SELECT market_rate, artifact_id FROM yoga WHERE market_rate > 272\")\n", "labels": {"reads": [{"table": "yoga", "columns": ["market_rate", "artifact_id"]}], "writes": [{"table": "recycling_stats", "columns": ["market_rate", "artifact_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"initiatives\")\nsrc.write.insertInto(\"totalenergyproduction\", overwrite=True)\n", "labels": {"reads": [{"table": "initiatives", "columns": null}], "writes": [{"table": "totalenergyproduction", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM plankton\", conn)\ndf.to_sql(\"volunteerhours\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "plankton", "columns": null}], "writes": [{"table": "volunteerhours", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO store SELECT suppliername, date_payment_made FROM fields WHERE suppliername > 33\"\n", "labels": {"reads": [{"table": "fields", "columns": ["suppliername", "date_payment_made"]}], "writes": [{"table": "store", "columns": ["suppliername", "date_payment_made"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM financial_capability\", conn)\ndf.to_sql(\"dw.dw_payments_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "financial_capability", "columns": null}], "writes": [{"table": "dw.dw_payments_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM global_sales_2022\", conn)\ndf.to_sql(\"publicchargingstations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}], "writes": [{"table": "publicchargingstations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO program_funding_2 SELECT a.principal_activities, b.iata FROM economic_diversification a JOIN strains b ON a.oil_production = b.oil_production\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "economic_diversification", "columns": null}, {"table": "strains", "columns": null}], "writes": [{"table": "program_funding_2", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"initiatives\")\nsrc.write.insertInto(\"space_exploration\", overwrite=True)\n", "labels": {"reads": [{"table": "initiatives", "columns": null}], "writes": [{"table": "space_exploration", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT surname, observation_id FROM forms LIMIT 217\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO bi.bi_inventory_full SELECT school_id, school_code, productivity FROM chemicals_annual WHERE school_id > 12\")\n", "labels": {"reads": [{"table": "forms", "columns": ["surname", "observation_id"]}, {"table": "chemicals_annual", "columns": ["school_id", "school_code", "productivity"]}], "writes": [{"table": "bi.bi_inventory_full", "columns": ["school_id", "school_code", "productivity"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT professionalid, visitid FROM ocean_temperatures\", engine)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"customer_address_history\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ocean_temperatures", "columns": ["professionalid", "visitid"]}], "writes": [{"table": "customer_address_history", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO paris_train SELECT * FROM legacy\ncur.execute(\"SELECT platformid, classtype FROM hotel_tech_adoptions LIMIT 133\")\n", "labels": {"reads": [{"table": "hotel_tech_adoptions", "columns": ["platformid", "classtype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"machinery\").toPandas()\ndf[[\"problem_description\", \"testtypeid\"]].to_sql(\"mart.mart_device_log\", engine, index=False)\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": [{"table": "mart.mart_device_log", "columns": ["problem_description", "testtypeid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_users_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT donortype, album FROM total_consumption LIMIT 114;\nINSERT INTO station_company SELECT participant_type_code, equipment_type, booking_end_date, trial_id FROM mart.shipments_delta WHERE participant_type_code > 270;\nSQL\n", "labels": {"reads": [{"table": "total_consumption", "columns": ["donortype", "album"]}, {"table": "mart.shipments_delta", "columns": ["participant_type_code", "equipment_type", "booking_end_date", "trial_id"]}], "writes": [{"table": "station_company", "columns": ["participant_type_code", "equipment_type", "booking_end_date", "trial_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT garment_id, value FROM inventory LIMIT 87\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO people SELECT sector_id, route_name, event_id FROM satellitedata WHERE sector_id > 158\")\n", "labels": {"reads": [{"table": "inventory", "columns": ["garment_id", "value"]}, {"table": "satellitedata", "columns": ["sector_id", "route_name", "event_id"]}], "writes": [{"table": "people", "columns": ["sector_id", "route_name", "event_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO astronauts SELECT vendorname, participated_in_open_pedagogy FROM investment WHERE vendorname > 222\"\n", "labels": {"reads": [{"table": "investment", "columns": ["vendorname", "participated_in_open_pedagogy"]}], "writes": [{"table": "astronauts", "columns": ["vendorname", "participated_in_open_pedagogy"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM therapy_attendance\"\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dws_inventory_di depends on seamounts\ndbt build --models dws_inventory_di --vars '{\"src\":\"seamounts\"}'\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": [{"table": "dws_inventory_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO malicious_activity SELECT mine_type, vulnerability_name FROM industry_funding WHERE mine_type > 406\"], check=True)\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["mine_type", "vulnerability_name"]}], "writes": [{"table": "malicious_activity", "columns": ["mine_type", "vulnerability_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sitem SELECT plant_name, scan_date, garment_type FROM ma_inspections WHERE plant_name > 427;\nEOF\n", "labels": {"reads": [{"table": "ma_inspections", "columns": ["plant_name", "scan_date", "garment_type"]}], "writes": [{"table": "sitem", "columns": ["plant_name", "scan_date", "garment_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spending\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.inventory_di\")\n", "labels": {"reads": [{"table": "spending", "columns": null}], "writes": [{"table": "ads.inventory_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.staff_id > 253).all()\n# src table: vehicle_registrations\nengine.execute(\"INSERT INTO investments SELECT * FROM vehicle_registrations\")\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": null}], "writes": [{"table": "investments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mediterranean_salinity\", conn)\ndf.to_sql(\"mentalhealthscores\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": null}], "writes": [{"table": "mentalhealthscores", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT faculty_id, member_in_charge_id FROM shipment LIMIT 358;\nINSERT INTO security_incidents SELECT editor_id, amount_due FROM waterconsumptionbyoperation WHERE editor_id > 296;\nSQL\n", "labels": {"reads": [{"table": "shipment", "columns": ["faculty_id", "member_in_charge_id"]}, {"table": "waterconsumptionbyoperation", "columns": ["editor_id", "amount_due"]}], "writes": [{"table": "security_incidents", "columns": ["editor_id", "amount_due"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT production_date, calendar FROM ads.ads_cart_item_hourly LIMIT 464;\nINSERT INTO reporters SELECT num_shariah_compliant_investments, quantity_sold, main_services, mission_id FROM reservations WHERE num_shariah_compliant_investments > 335;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": ["production_date", "calendar"]}, {"table": "reservations", "columns": ["num_shariah_compliant_investments", "quantity_sold", "main_services", "mission_id"]}], "writes": [{"table": "reporters", "columns": ["num_shariah_compliant_investments", "quantity_sold", "main_services", "mission_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO invoice_lines SELECT a.quantity_sold, b.borough FROM trainingprograms a JOIN vesselfuel b ON a.years_played = b.years_played\"\n", "labels": {"reads": [{"table": "trainingprograms", "columns": null}, {"table": "vesselfuel", "columns": null}], "writes": [{"table": "invoice_lines", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table classicgame --target-dir /tmp/land\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT daily_consumption, eventname FROM uel_top10 LIMIT 469\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["daily_consumption", "eventname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table animals --columns inclusivehousing,digital --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "animals", "columns": ["inclusivehousing", "digital"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO properties SELECT studio_name, claim_id, review_score, typical_buying_price FROM uel_top10 WHERE studio_name > 349\"\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["studio_name", "claim_id", "review_score", "typical_buying_price"]}], "writes": [{"table": "properties", "columns": ["studio_name", "claim_id", "review_score", "typical_buying_price"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO field5 SELECT primaryaffiliation, complaint_type_code FROM az_drought_impact WHERE primaryaffiliation > 222;\nEOF\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": ["primaryaffiliation", "complaint_type_code"]}], "writes": [{"table": "field5", "columns": ["primaryaffiliation", "complaint_type_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cases_count, sustainability_score FROM stg.stg_exposure_daily LIMIT 212\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO art_workshops SELECT max_temperature_f, class_senator_vote, restaurantname FROM disabilitysupportprograms WHERE max_temperature_f > 134\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": ["cases_count", "sustainability_score"]}, {"table": "disabilitysupportprograms", "columns": ["max_temperature_f", "class_senator_vote", "restaurantname"]}], "writes": [{"table": "art_workshops", "columns": ["max_temperature_f", "class_senator_vote", "restaurantname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd_events_delta SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO roles SELECT institution_name, store_id FROM cinema WHERE institution_name > 246\"], check=True)\n", "labels": {"reads": [{"table": "cinema", "columns": ["institution_name", "store_id"]}], "writes": [{"table": "roles", "columns": ["institution_name", "store_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO taj_mahal_visitors SELECT main_industry, porphyria, major, factory_name FROM pipelines WHERE main_industry > 40\")\n", "labels": {"reads": [{"table": "pipelines", "columns": ["main_industry", "porphyria", "major", "factory_name"]}], "writes": [{"table": "taj_mahal_visitors", "columns": ["main_industry", "porphyria", "major", "factory_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"stg.member_point_df\")\nsink_to_store(df, \"disasters\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.member_point_df", "columns": null}], "writes": [{"table": "disasters", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO yttrium_production SELECT a.ingredient, b.wildlife_type_id FROM skills a JOIN usdaviolations b ON a.employee_id = b.employee_id\"\n", "labels": {"reads": [{"table": "skills", "columns": null}, {"table": "usdaviolations", "columns": null}], "writes": [{"table": "yttrium_production", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.drugname > 255).all()\n# src table: labor_statistics\nengine.execute(\"INSERT INTO innovation_trends SELECT * FROM labor_statistics\")\n", "labels": {"reads": [{"table": "labor_statistics", "columns": null}], "writes": [{"table": "innovation_trends", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"art_collection\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "art_collection", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO permit SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO player_college SELECT materialtype, ai_adoption_date, asset_make, investment FROM stg.campaigns_df WHERE materialtype > 388;\nEOF\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": ["materialtype", "ai_adoption_date", "asset_make", "investment"]}], "writes": [{"table": "player_college", "columns": ["materialtype", "ai_adoption_date", "asset_make", "investment"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model spacecrafts depends on vehicle\ndbt run --select spacecrafts --vars '{\"src\":\"vehicle\"}'\n", "labels": {"reads": [{"table": "vehicle", "columns": null}], "writes": [{"table": "spacecrafts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safe_dataset\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "safe_dataset", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO sustainableprojects SELECT uk_vat_number, left_office, savingsid FROM sustainable_building WHERE uk_vat_number > 45\"\n", "labels": {"reads": [{"table": "sustainable_building", "columns": ["uk_vat_number", "left_office", "savingsid"]}], "writes": [{"table": "sustainableprojects", "columns": ["uk_vat_number", "left_office", "savingsid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table trafficviolations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "trafficviolations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO animals SELECT certification, unit_id, statement_id FROM otas WHERE certification > 207\"], check=True)\n", "labels": {"reads": [{"table": "otas", "columns": ["certification", "unit_id", "statement_id"]}], "writes": [{"table": "animals", "columns": ["certification", "unit_id", "statement_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT complaintid, furniture_id FROM cargos LIMIT 281\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO zipcodes SELECT manager_name, cargo_weight, sustainable, tech_id FROM waste_data WHERE manager_name > 205\")\n", "labels": {"reads": [{"table": "cargos", "columns": ["complaintid", "furniture_id"]}, {"table": "waste_data", "columns": ["manager_name", "cargo_weight", "sustainable", "tech_id"]}], "writes": [{"table": "zipcodes", "columns": ["manager_name", "cargo_weight", "sustainable", "tech_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO membership_register_branch SELECT certification, dysprosium_prod, stock FROM sites WHERE certification > 196\"\n", "labels": {"reads": [{"table": "sites", "columns": ["certification", "dysprosium_prod", "stock"]}], "writes": [{"table": "membership_register_branch", "columns": ["certification", "dysprosium_prod", "stock"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table traffic_violations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "traffic_violations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO asia_events (exhibition_id, number_of_hosts) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "asia_events", "columns": ["exhibition_id", "number_of_hosts"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO canada_tech SELECT crime_rate, founder_identifies_as_lgbtq, garmentid, discovered_date FROM taj_mahal_visitors WHERE crime_rate > 207\"\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": ["crime_rate", "founder_identifies_as_lgbtq", "garmentid", "discovered_date"]}], "writes": [{"table": "canada_tech", "columns": ["crime_rate", "founder_identifies_as_lgbtq", "garmentid", "discovered_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"body_builder\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_cart_item_hourly\")\n", "labels": {"reads": [{"table": "body_builder", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM artsandcrafts\", conn)\ndf.to_sql(\"organicproducts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "artsandcrafts", "columns": null}], "writes": [{"table": "organicproducts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM infantmortalitydata\"\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM rigs\", conn)\ndf.to_sql(\"smartcitytech\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "rigs", "columns": null}], "writes": [{"table": "smartcitytech", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT base_id, ethnicity FROM papers\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"dw_payments\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "papers", "columns": ["base_id", "ethnicity"]}], "writes": [{"table": "dw_payments", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT teacher_id, patentexpirationdate FROM garments LIMIT 249\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "garments", "columns": ["teacher_id", "patentexpirationdate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donations_insert_2 SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO product SELECT * FROM legacy\ncur.execute(\"SELECT manager_id, genre FROM ingredientsvegancrueltyfree LIMIT 32\")\n", "labels": {"reads": [{"table": "ingredientsvegancrueltyfree", "columns": ["manager_id", "genre"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM global_tournament\", conn)\ndf.to_sql(\"genetics_stats.research_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "global_tournament", "columns": null}], "writes": [{"table": "genetics_stats.research_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stateinfrastructure SELECT * FROM legacy\ncur.execute(\"SELECT acc_bal, destination_id FROM therapy LIMIT 41\")\n", "labels": {"reads": [{"table": "therapy", "columns": ["acc_bal", "destination_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO eco_hotels (investor_name, review_text) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "eco_hotels", "columns": ["investor_name", "review_text"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lender_id, sustainabilityid FROM mine LIMIT 449\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO party_events SELECT maintenance_date, sea FROM donations2022 WHERE maintenance_date > 479\")\n", "labels": {"reads": [{"table": "mine", "columns": ["lender_id", "sustainabilityid"]}, {"table": "donations2022", "columns": ["maintenance_date", "sea"]}], "writes": [{"table": "party_events", "columns": ["maintenance_date", "sea"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT round_date, mappinglength FROM participates_in LIMIT 284\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "participates_in", "columns": ["round_date", "mappinglength"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO renewable_projects SELECT num_schools, measurement_date FROM public_transportation_routes WHERE num_schools > 55\")\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": ["num_schools", "measurement_date"]}], "writes": [{"table": "renewable_projects", "columns": ["num_schools", "measurement_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"ads.ads_inventory_df\")\nsave_to_target(df, \"emerging_markets.digital_assets\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.ads_inventory_df", "columns": null}], "writes": [{"table": "emerging_markets.digital_assets", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"visitor_exhibition\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO wildlife_sanctuaries SELECT grade, co2_reduction_tons, electoral_register_id FROM behavior_incident WHERE grade > 128\"\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["grade", "co2_reduction_tons", "electoral_register_id"]}], "writes": [{"table": "wildlife_sanctuaries", "columns": ["grade", "co2_reduction_tons", "electoral_register_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO climate_finance_re SELECT online_dispute_resolution, college_id, prereq_id, galleryid FROM exhibition_record WHERE online_dispute_resolution > 193\")\n", "labels": {"reads": [{"table": "exhibition_record", "columns": ["online_dispute_resolution", "college_id", "prereq_id", "galleryid"]}], "writes": [{"table": "climate_finance_re", "columns": ["online_dispute_resolution", "college_id", "prereq_id", "galleryid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods_member_point_full SELECT hours_played, framework_name FROM ads.ads_vendors_hourly WHERE hours_played > 202\"\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": ["hours_played", "framework_name"]}], "writes": [{"table": "ods_member_point_full", "columns": ["hours_played", "framework_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO vehicle SELECT * FROM legacy\ncur.execute(\"SELECT has_aloe_vera, resolved FROM course LIMIT 322\")\n", "labels": {"reads": [{"table": "course", "columns": ["has_aloe_vera", "resolved"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO transportation_union SELECT a.roomtype, b.menu_category FROM procedures a JOIN mart.mart_users b ON a.num_employees = b.num_employees\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "procedures", "columns": null}, {"table": "mart.mart_users", "columns": null}], "writes": [{"table": "transportation_union", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO stg.stg_products_full SELECT a.building_full_name, b.fanid FROM coowners a JOIN waste b ON a.membergender = b.membergender\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "coowners", "columns": null}, {"table": "waste", "columns": null}], "writes": [{"table": "stg.stg_products_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tickets_3\").toPandas()\ndf[[\"authors\", \"heritage_site_id\"]].to_sql(\"platformg\", engine, index=False)\n", "labels": {"reads": [{"table": "tickets_3", "columns": null}], "writes": [{"table": "platformg", "columns": ["authors", "heritage_site_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM wildlife\"\n", "labels": {"reads": [{"table": "wildlife", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"militarycyberops\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "militarycyberops", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO pipelines SELECT budget_million, country, assets, organisation_type_description FROM gender WHERE budget_million > 312;\nEOF\n", "labels": {"reads": [{"table": "gender", "columns": ["budget_million", "country", "assets", "organisation_type_description"]}], "writes": [{"table": "pipelines", "columns": ["budget_million", "country", "assets", "organisation_type_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"rural_hospitals\")\nexport_to_output(df, \"field_production\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "rural_hospitals", "columns": null}], "writes": [{"table": "field_production", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM areas\", conn)\ndf.to_sql(\"location\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "areas", "columns": null}], "writes": [{"table": "location", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO document_structures SELECT animal, date_complaint_raised FROM dwd_coupon_use_hourly WHERE animal > 410;\nEOF\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": ["animal", "date_complaint_raised"]}], "writes": [{"table": "document_structures", "columns": ["animal", "date_complaint_raised"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ref_calendar\")\nsrc.write.insertInto(\"hires\", overwrite=True)\n", "labels": {"reads": [{"table": "ref_calendar", "columns": null}], "writes": [{"table": "hires", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table vehicle_registrations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"infra_diversification\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model consumer_preference depends on efforts\ndbt build --models consumer_preference --vars '{\"src\":\"efforts\"}'\n", "labels": {"reads": [{"table": "efforts", "columns": null}], "writes": [{"table": "consumer_preference", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"price_data\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"music\")\n", "labels": {"reads": [{"table": "price_data", "columns": null}], "writes": [{"table": "music", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model musicgenre depends on debris\ndbt run --models musicgenre --vars '{\"source_table\":\"debris\"}'\n", "labels": {"reads": [{"table": "debris", "columns": null}], "writes": [{"table": "musicgenre", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO musical (roomid, fish_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "musical", "columns": ["roomid", "fish_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO match SELECT artwork, event_type_id, individual_id, hometeam FROM gamesessions WHERE artwork > 443;\nEOF\n", "labels": {"reads": [{"table": "gamesessions", "columns": ["artwork", "event_type_id", "individual_id", "hometeam"]}], "writes": [{"table": "match", "columns": ["artwork", "event_type_id", "individual_id", "hometeam"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO volunteerhours SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"climate_finance_organizations\")\nsrc.write.insertInto(\"fireincidents\", overwrite=True)\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": [{"table": "fireincidents", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO healthcare_centers SELECT union_member_id, floor_exercise_points, attack_country FROM blockchain_tech WHERE union_member_id > 118\"\n", "labels": {"reads": [{"table": "blockchain_tech", "columns": ["union_member_id", "floor_exercise_points", "attack_country"]}], "writes": [{"table": "healthcare_centers", "columns": ["union_member_id", "floor_exercise_points", "attack_country"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"european_healthcare\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ports\")\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}], "writes": [{"table": "ports", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT payment_method_code, ironquantity FROM marine_life_research LIMIT 28;\nINSERT INTO animal_budget SELECT museum, negotiation_date FROM host WHERE museum > 217;\nSQL\n", "labels": {"reads": [{"table": "marine_life_research", "columns": ["payment_method_code", "ironquantity"]}, {"table": "host", "columns": ["museum", "negotiation_date"]}], "writes": [{"table": "animal_budget", "columns": ["museum", "negotiation_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.refunds_daily --columns bus_id,policyholderid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.refunds_daily", "columns": ["bus_id", "policyholderid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO drug_approval SELECT product, medical_professional_id FROM artsales WHERE product > 370\")\n", "labels": {"reads": [{"table": "artsales", "columns": ["product", "medical_professional_id"]}], "writes": [{"table": "drug_approval", "columns": ["product", "medical_professional_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads_refunds_full SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_projects_sales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tree_species\")\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": null}], "writes": [{"table": "tree_species", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT subscribe_date, month FROM body_builder LIMIT 54;\nINSERT INTO dailyapplestreams SELECT bookings, trip_end_time FROM train_station WHERE bookings > 265;\nSQL\n", "labels": {"reads": [{"table": "body_builder", "columns": ["subscribe_date", "month"]}, {"table": "train_station", "columns": ["bookings", "trip_end_time"]}], "writes": [{"table": "dailyapplestreams", "columns": ["bookings", "trip_end_time"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO professionals SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO network_infrastructure SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ingredients --columns rate,attendees --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ingredients", "columns": ["rate", "attendees"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO design_standards SELECT tonnage, course_completion, winning_pilot, professional_development_programs FROM new_schedules WHERE tonnage > 114\"\n", "labels": {"reads": [{"table": "new_schedules", "columns": ["tonnage", "course_completion", "winning_pilot", "professional_development_programs"]}], "writes": [{"table": "design_standards", "columns": ["tonnage", "course_completion", "winning_pilot", "professional_development_programs"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO daily_production SELECT customer, skill_id, yield FROM drivers WHERE customer > 182\"\n", "labels": {"reads": [{"table": "drivers", "columns": ["customer", "skill_id", "yield"]}], "writes": [{"table": "daily_production", "columns": ["customer", "skill_id", "yield"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mine_workforce (policy_id, productionid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mine_workforce", "columns": ["policy_id", "productionid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO discount_coupons (authors, school) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "discount_coupons", "columns": ["authors", "school"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO measurements SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trainingyear, class_section FROM artifactanalysis LIMIT 367\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": ["trainingyear", "class_section"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT contract_count, lastname FROM spaceexploration LIMIT 496\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "spaceexploration", "columns": ["contract_count", "lastname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT products_this_year, trip_end_time FROM underwater_cables LIMIT 362\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO textileworkers SELECT invested, treasurer_vote, game_genre FROM spaceexploration WHERE invested > 362\")\n", "labels": {"reads": [{"table": "underwater_cables", "columns": ["products_this_year", "trip_end_time"]}, {"table": "spaceexploration", "columns": ["invested", "treasurer_vote", "game_genre"]}], "writes": [{"table": "textileworkers", "columns": ["invested", "treasurer_vote", "game_genre"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO feed SELECT a.nurse, b.appointment_date FROM ods.ods_campaigns_delta a JOIN district_schools b ON a.brandid = b.brandid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": null}, {"table": "district_schools", "columns": null}], "writes": [{"table": "feed", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO calibration_data2 SELECT a.hardware_colours, b.satelliteid FROM australia_offset_programs a JOIN labor_hours b ON a.founder_lgbtq = b.founder_lgbtq\"\n", "labels": {"reads": [{"table": "australia_offset_programs", "columns": null}, {"table": "labor_hours", "columns": null}], "writes": [{"table": "calibration_data2", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model totalenergyproduction depends on policyimpact\ndbt run --select totalenergyproduction --vars 'source: policyimpact'\n", "labels": {"reads": [{"table": "policyimpact", "columns": null}], "writes": [{"table": "totalenergyproduction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO train_maintenance SELECT petid, dob FROM nasa_mars_program WHERE petid > 395;\nEOF\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": ["petid", "dob"]}], "writes": [{"table": "train_maintenance", "columns": ["petid", "dob"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM traffic\"\n", "labels": {"reads": [{"table": "traffic", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"pets\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"habitat3\")\n", "labels": {"reads": [{"table": "pets", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"bi.bi_events_daily\")\ndump_to_sink(df, \"ref_document_status\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": [{"table": "ref_document_status", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM safety_research\"\n", "labels": {"reads": [{"table": "safety_research", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dw_payments SELECT maintenancedate, donator_name FROM ads_cart_item_hourly WHERE maintenancedate > 32\"\n", "labels": {"reads": [{"table": "ads_cart_item_hourly", "columns": ["maintenancedate", "donator_name"]}], "writes": [{"table": "dw_payments", "columns": ["maintenancedate", "donator_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"decentralized_apps\")\nsrc.write.insertInto(\"dwd_payments_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "decentralized_apps", "columns": null}], "writes": [{"table": "dwd_payments_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM performances\"\n", "labels": {"reads": [{"table": "performances", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.destination_state > 58).all()\n# src table: field_production\nengine.execute(\"INSERT INTO artworks SELECT * FROM field_production\")\n", "labels": {"reads": [{"table": "field_production", "columns": null}], "writes": [{"table": "artworks", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT orderid, last_service FROM bi.bi_payments_delta LIMIT 186;\nINSERT INTO deep_sea_expeditions SELECT dispensaryid, menuid, total_amount FROM affordablehousing WHERE dispensaryid > 390;\nSQL\n", "labels": {"reads": [{"table": "bi.bi_payments_delta", "columns": ["orderid", "last_service"]}, {"table": "affordablehousing", "columns": ["dispensaryid", "menuid", "total_amount"]}], "writes": [{"table": "deep_sea_expeditions", "columns": ["dispensaryid", "menuid", "total_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model program_outcomes depends on autonomousvehicles\ndbt build --select program_outcomes --vars 'source: autonomousvehicles'\n", "labels": {"reads": [{"table": "autonomousvehicles", "columns": null}], "writes": [{"table": "program_outcomes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.paperid > 198).all()\n# src table: recyclednylongarments\nengine.execute(\"INSERT INTO biodiversity SELECT * FROM recyclednylongarments\")\n", "labels": {"reads": [{"table": "recyclednylongarments", "columns": null}], "writes": [{"table": "biodiversity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibition_visits\").toPandas()\ndf[[\"organisation_id\", \"method_id\"]].to_sql(\"jupiter_missions\", engine, index=False)\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": null}], "writes": [{"table": "jupiter_missions", "columns": ["organisation_id", "method_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainability_fact\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO address SELECT * FROM legacy\ncur.execute(\"SELECT card_number, amount_donated FROM tourist_destinations LIMIT 289\")\n", "labels": {"reads": [{"table": "tourist_destinations", "columns": ["card_number", "amount_donated"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"smartcities\")\ndump_to_target(df, \"productivity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "smartcities", "columns": null}], "writes": [{"table": "productivity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO oceanography SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO euroavev SELECT tourist_id, metric_id, violation_type, component_name FROM food_justice WHERE tourist_id > 215\")\n", "labels": {"reads": [{"table": "food_justice", "columns": ["tourist_id", "metric_id", "violation_type", "component_name"]}], "writes": [{"table": "euroavev", "columns": ["tourist_id", "metric_id", "violation_type", "component_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table life_expectancy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "life_expectancy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"workersalaries\")\nsrc.write.insertInto(\"section\", overwrite=True)\n", "labels": {"reads": [{"table": "workersalaries", "columns": null}], "writes": [{"table": "section", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT dispensary_id, postal_code FROM livestock LIMIT 42\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ca_menu_items SELECT invoice_details, missionid, dock_status FROM energy_production WHERE invoice_details > 76\")\n", "labels": {"reads": [{"table": "livestock", "columns": ["dispensary_id", "postal_code"]}, {"table": "energy_production", "columns": ["invoice_details", "missionid", "dock_status"]}], "writes": [{"table": "ca_menu_items", "columns": ["invoice_details", "missionid", "dock_status"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.ods_coupon_use_di SELECT a.workshop_id, b.submission_id FROM contract_timeline a JOIN gamesales b ON a.unavailable = b.unavailable\"\n", "labels": {"reads": [{"table": "contract_timeline", "columns": null}, {"table": "gamesales", "columns": null}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT catalog_entry_id, co2_reduction_tons FROM livestock LIMIT 106\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "livestock", "columns": ["catalog_entry_id", "co2_reduction_tons"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO languages SELECT item, extraction_date FROM workplace_safety WHERE item > 414;\nEOF\n", "labels": {"reads": [{"table": "workplace_safety", "columns": ["item", "extraction_date"]}], "writes": [{"table": "languages", "columns": ["item", "extraction_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"first_notification_of_loss\")\nsrc.write.insertInto(\"investments\", overwrite=True)\n", "labels": {"reads": [{"table": "first_notification_of_loss", "columns": null}], "writes": [{"table": "investments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO daily_transaction_volume SELECT building_full_name, centername FROM baseball_teams WHERE building_full_name > 91\"\n", "labels": {"reads": [{"table": "baseball_teams", "columns": ["building_full_name", "centername"]}], "writes": [{"table": "daily_transaction_volume", "columns": ["building_full_name", "centername"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT trainingname, average_age FROM ods.sessions_daily LIMIT 500\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO location SELECT date_of_latest_logon, order_id FROM dw.dw_member_point_hourly WHERE date_of_latest_logon > 19\")\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": ["trainingname", "average_age"]}, {"table": "dw.dw_member_point_hourly", "columns": ["date_of_latest_logon", "order_id"]}], "writes": [{"table": "location", "columns": ["date_of_latest_logon", "order_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO flight_safety SELECT bname, first_name, communityid, ihsaa_football_class FROM sustainabilityratings WHERE bname > 167\"\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": ["bname", "first_name", "communityid", "ihsaa_football_class"]}], "writes": [{"table": "flight_safety", "columns": ["bname", "first_name", "communityid", "ihsaa_football_class"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hearingdate, student_details FROM excavationsites LIMIT 303;\nINSERT INTO chemical_production_3 SELECT vendorid, average, subject FROM ods.ods_exposure_delta WHERE vendorid > 110;\nSQL\n", "labels": {"reads": [{"table": "excavationsites", "columns": ["hearingdate", "student_details"]}, {"table": "ods.ods_exposure_delta", "columns": ["vendorid", "average", "subject"]}], "writes": [{"table": "chemical_production_3", "columns": ["vendorid", "average", "subject"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO animal_species SELECT a.primary_advisor, b.trial_success_rate FROM recall_reports a JOIN smart_city_projects b ON a.vehicle_id = b.vehicle_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recall_reports", "columns": null}, {"table": "smart_city_projects", "columns": null}], "writes": [{"table": "animal_species", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO consumer SELECT a.dispensaryid, b.highscore FROM circular_economy a JOIN projects b ON a.mine_name = b.mine_name\"\n", "labels": {"reads": [{"table": "circular_economy", "columns": null}, {"table": "projects", "columns": null}], "writes": [{"table": "consumer", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO event_attendance SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM volunteer_signups\", conn)\ndf.to_sql(\"song\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "volunteer_signups", "columns": null}], "writes": [{"table": "song", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT committee, menuname FROM safety_violations LIMIT 116\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO airlines SELECT created_date, songid, unionid FROM maintenance_requests WHERE created_date > 201\")\n", "labels": {"reads": [{"table": "safety_violations", "columns": ["committee", "menuname"]}, {"table": "maintenance_requests", "columns": ["created_date", "songid", "unionid"]}], "writes": [{"table": "airlines", "columns": ["created_date", "songid", "unionid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO taj_mahal_visitors SELECT retailer_id, color_description, resolution FROM salary WHERE retailer_id > 494\"], check=True)\n", "labels": {"reads": [{"table": "salary", "columns": ["retailer_id", "color_description", "resolution"]}], "writes": [{"table": "taj_mahal_visitors", "columns": ["retailer_id", "color_description", "resolution"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO displaced_people SELECT trip_id, recycling_rate FROM dw.dw_payments_full WHERE trip_id > 385\")\n", "labels": {"reads": [{"table": "dw.dw_payments_full", "columns": ["trip_id", "recycling_rate"]}], "writes": [{"table": "displaced_people", "columns": ["trip_id", "recycling_rate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel SELECT registered_date, reaction_time FROM water_usage WHERE registered_date > 66\"\n", "labels": {"reads": [{"table": "water_usage", "columns": ["registered_date", "reaction_time"]}], "writes": [{"table": "vessel", "columns": ["registered_date", "reaction_time"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO atlantic_plate SELECT a.portname, b.menu_type FROM device_usage a JOIN ucl_top10 b ON a.mhw_id = b.mhw_id\"\n", "labels": {"reads": [{"table": "device_usage", "columns": null}, {"table": "ucl_top10", "columns": null}], "writes": [{"table": "atlantic_plate", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model exam_results depends on drills\ndbt build -s exam_results --vars '{\"src\":\"drills\"}'\n", "labels": {"reads": [{"table": "drills", "columns": null}], "writes": [{"table": "exam_results", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"genre\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sustainable_menu_items\")\n", "labels": {"reads": [{"table": "genre", "columns": null}], "writes": [{"table": "sustainable_menu_items", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dorm depends on textileworkers\ndbt build --models dorm --vars 'source: textileworkers'\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": [{"table": "dorm", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO water_sources SELECT * FROM legacy\ncur.execute(\"SELECT customer_first_name, zone FROM collections LIMIT 226\")\n", "labels": {"reads": [{"table": "collections", "columns": ["customer_first_name", "zone"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO musical (tourist_id, issue_month) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "musical", "columns": ["tourist_id", "issue_month"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.inventory_delta\").toPandas()\ndf[[\"calendar\", \"nurse\"]].to_sql(\"chemical_processes\", engine, index=False)\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": [{"table": "chemical_processes", "columns": ["calendar", "nurse"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cmi_cross_references\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"infrastructure_projects\")\n", "labels": {"reads": [{"table": "cmi_cross_references", "columns": null}], "writes": [{"table": "infrastructure_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO strains (production, news_story_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "strains", "columns": ["production", "news_story_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_payments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"visits\")\n", "labels": {"reads": [{"table": "dwd.dwd_payments", "columns": null}], "writes": [{"table": "visits", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO innovation_metrics SELECT date_of_birth, element, spill_name, date_became_customer FROM healthcareaccess WHERE date_of_birth > 35;\nEOF\n", "labels": {"reads": [{"table": "healthcareaccess", "columns": ["date_of_birth", "element", "spill_name", "date_became_customer"]}], "writes": [{"table": "innovation_metrics", "columns": ["date_of_birth", "element", "spill_name", "date_became_customer"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.detention_type_description > 278).all()\n# src table: investment_rounds\nengine.execute(\"INSERT INTO labor_hours SELECT * FROM investment_rounds\")\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "labor_hours", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO african_union_countries SELECT partitionid, fault_short_name, initiative_type FROM station_company WHERE partitionid > 135\")\n", "labels": {"reads": [{"table": "station_company", "columns": ["partitionid", "fault_short_name", "initiative_type"]}], "writes": [{"table": "african_union_countries", "columns": ["partitionid", "fault_short_name", "initiative_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO road_construction SELECT contactid, assessmentdate, mining_operation, donor FROM indie_artists WHERE contactid > 91\")\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["contactid", "assessmentdate", "mining_operation", "donor"]}], "writes": [{"table": "road_construction", "columns": ["contactid", "assessmentdate", "mining_operation", "donor"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO shared_rides_tokyo SELECT mailing_date, foreign, advocate_name FROM donation WHERE mailing_date > 155\"\n", "labels": {"reads": [{"table": "donation", "columns": ["mailing_date", "foreign", "advocate_name"]}], "writes": [{"table": "shared_rides_tokyo", "columns": ["mailing_date", "foreign", "advocate_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"companies\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"fair_trade_brands\")\n", "labels": {"reads": [{"table": "companies", "columns": null}], "writes": [{"table": "fair_trade_brands", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO climate_adaptation_re SELECT * FROM legacy\ncur.execute(\"SELECT low_estimate, phase FROM music_festival LIMIT 185\")\n", "labels": {"reads": [{"table": "music_festival", "columns": ["low_estimate", "phase"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_users_hourly SELECT * FROM legacy\ncur.execute(\"SELECT media_outlet, maintenance_id FROM crypto_transactions LIMIT 397\")\n", "labels": {"reads": [{"table": "crypto_transactions", "columns": ["media_outlet", "maintenance_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"eu_data_usage\")\nsrc.write.insertInto(\"mart.campaigns_di\", overwrite=True)\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": null}], "writes": [{"table": "mart.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO paris_train SELECT * FROM legacy\ncur.execute(\"SELECT contract_start_date, total_value_purchased FROM vehicle_registrations LIMIT 239\")\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": ["contract_start_date", "total_value_purchased"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart_exposure_di SELECT a.organization_id, b.permitid FROM teachers a JOIN nasa_mars_program b ON a.numhearings = b.numhearings\"\n", "labels": {"reads": [{"table": "teachers", "columns": null}, {"table": "nasa_mars_program", "columns": null}], "writes": [{"table": "mart_exposure_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model caribbeansea depends on emerging_markets.digital_assets\ndbt run --models caribbeansea --vars '{\"source_table\":\"emerging_markets.digital_assets\"}'\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": null}], "writes": [{"table": "caribbeansea", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table sector_incidents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sector_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO defense_contracts SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ads_vendors_hourly depends on stg.events_hourly\ndbt run -s ads_vendors_hourly --vars '{\"src\":\"stg.events_hourly\"}'\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": null}], "writes": [{"table": "ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"assets\")\nsink_to_sink(df, \"water_sources\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": [{"table": "water_sources", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.inventory_df SELECT 1\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table defenseprojects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_payments_delta SELECT * FROM legacy\ncur.execute(\"SELECT business_size, city FROM mart.mart_sessions_di LIMIT 41\")\n", "labels": {"reads": [{"table": "mart.mart_sessions_di", "columns": ["business_size", "city"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw_vendors_di SELECT * FROM legacy\ncur.execute(\"SELECT game, enrollment_date FROM traditionalarts LIMIT 170\")\n", "labels": {"reads": [{"table": "traditionalarts", "columns": ["game", "enrollment_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO clinic_2022 SELECT deliveryaddress, length_feet FROM dws.dws_orders WHERE deliveryaddress > 319\")\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": ["deliveryaddress", "length_feet"]}], "writes": [{"table": "clinic_2022", "columns": ["deliveryaddress", "length_feet"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.mart_products_hourly (prof_num, origin) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_products_hourly", "columns": ["prof_num", "origin"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"euroavev\")\nsink_to_sink(df, \"smart_grids\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "euroavev", "columns": null}], "writes": [{"table": "smart_grids", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"race_ethnicity\").toPandas()\ndf[[\"health_equity_metric_2\", \"item\"]].to_sql(\"nailpolishsales\", engine, index=False)\n", "labels": {"reads": [{"table": "race_ethnicity", "columns": null}], "writes": [{"table": "nailpolishsales", "columns": ["health_equity_metric_2", "item"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table hotel_chains --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hotel_chains", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO staff_roles (emp_jobcode, year_opened) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "staff_roles", "columns": ["emp_jobcode", "year_opened"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO epl_teams SELECT completed, menu_name, incident, lat FROM renewables.renewable_projects WHERE completed > 204\"], check=True)\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": ["completed", "menu_name", "incident", "lat"]}], "writes": [{"table": "epl_teams", "columns": ["completed", "menu_name", "incident", "lat"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO user_stats SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO nba SELECT num_projects, shipment_year FROM tracks WHERE num_projects > 459\")\n", "labels": {"reads": [{"table": "tracks", "columns": ["num_projects", "shipment_year"]}], "writes": [{"table": "nba", "columns": ["num_projects", "shipment_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO expenses SELECT a.retweets, b.total_donation_amount FROM trees a JOIN collections b ON a.decision = b.decision\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trees", "columns": null}, {"table": "collections", "columns": null}], "writes": [{"table": "expenses", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM market_trends\", conn)\ndf.to_sql(\"royal_family\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "market_trends", "columns": null}], "writes": [{"table": "royal_family", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO urban_transportation SELECT a.network_name, b.violation_id FROM engineer_visits a JOIN arrivals b ON a.available_yn = b.available_yn\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "engineer_visits", "columns": null}, {"table": "arrivals", "columns": null}], "writes": [{"table": "urban_transportation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.open_year > 394).all()\n# src table: broadband_plans\nengine.execute(\"INSERT INTO stars SELECT * FROM broadband_plans\")\n", "labels": {"reads": [{"table": "broadband_plans", "columns": null}], "writes": [{"table": "stars", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dysprosium_mines\"\n", "labels": {"reads": [{"table": "dysprosium_mines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM financialwellbeing\", conn)\ndf.to_sql(\"shariah_compliant_finance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": null}], "writes": [{"table": "shariah_compliant_finance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table bi.bi_inventory_di --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_inventory_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"arcticwildlifereserve\").toPandas()\ndf[[\"purchase_details\", \"studio\"]].to_sql(\"florida_conservation_initiatives\", engine, index=False)\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": ["purchase_details", "studio"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tasks\").toPandas()\ndf[[\"sentence_id\", \"museum\"]].to_sql(\"call_volume\", engine, index=False)\n", "labels": {"reads": [{"table": "tasks", "columns": null}], "writes": [{"table": "call_volume", "columns": ["sentence_id", "museum"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO publicchargingstations SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sport_id, kills FROM basketball_match LIMIT 102;\nINSERT INTO pacific_ocean SELECT membership_id, component_name, type_of_thing_code FROM mart.mart_users_delta WHERE membership_id > 394;\nSQL\n", "labels": {"reads": [{"table": "basketball_match", "columns": ["sport_id", "kills"]}, {"table": "mart.mart_users_delta", "columns": ["membership_id", "component_name", "type_of_thing_code"]}], "writes": [{"table": "pacific_ocean", "columns": ["membership_id", "component_name", "type_of_thing_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO video_games (extraction_date, contributorname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "video_games", "columns": ["extraction_date", "contributorname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO crypto_transactions SELECT statename, market_share, meal_name FROM pilot WHERE statename > 233\"\n", "labels": {"reads": [{"table": "pilot", "columns": ["statename", "market_share", "meal_name"]}], "writes": [{"table": "crypto_transactions", "columns": ["statename", "market_share", "meal_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO project_timelines SELECT shipment_tracking_number, student FROM exhibition_visits WHERE shipment_tracking_number > 161\"], check=True)\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": ["shipment_tracking_number", "student"]}], "writes": [{"table": "project_timelines", "columns": ["shipment_tracking_number", "student"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT savings, employee_id FROM council_tax\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"ingredient\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "council_tax", "columns": ["savings", "employee_id"]}], "writes": [{"table": "ingredient", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO behavior_incident SELECT labordate, artifact_id, vessel_id FROM visitordemographics WHERE labordate > 351\"\n", "labels": {"reads": [{"table": "visitordemographics", "columns": ["labordate", "artifact_id", "vessel_id"]}], "writes": [{"table": "behavior_incident", "columns": ["labordate", "artifact_id", "vessel_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewableenergyprojects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"opendatainitiatives\")\n", "labels": {"reads": [{"table": "renewableenergyprojects", "columns": null}], "writes": [{"table": "opendatainitiatives", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT negative, genderid FROM eu_data_usage LIMIT 38;\nINSERT INTO maintenance SELECT dependent_name, planned_delivery_date, official_native_language, playtime FROM band WHERE dependent_name > 231;\nSQL\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["negative", "genderid"]}, {"table": "band", "columns": ["dependent_name", "planned_delivery_date", "official_native_language", "playtime"]}], "writes": [{"table": "maintenance", "columns": ["dependent_name", "planned_delivery_date", "official_native_language", "playtime"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO government.region SELECT a.dnumber, b.movie FROM publication a JOIN smartcontracts b ON a.hireid = b.hireid\"\n", "labels": {"reads": [{"table": "publication", "columns": null}, {"table": "smartcontracts", "columns": null}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"climateresearch\")\nsrc.write.insertInto(\"ods_shipments_df\", overwrite=True)\n", "labels": {"reads": [{"table": "climateresearch", "columns": null}], "writes": [{"table": "ods_shipments_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO scientists SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"reporters\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "reporters", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT years_working, year FROM regulatory_compliance\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ndf.to_sql(\"safety_incident\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "regulatory_compliance", "columns": ["years_working", "year"]}], "writes": [{"table": "safety_incident", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO safety_testing SELECT a.section_id, b.editor_id FROM london.stations a JOIN co2emissions b ON a.wellname = b.wellname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "london.stations", "columns": null}, {"table": "co2emissions", "columns": null}], "writes": [{"table": "safety_testing", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd.dwd_orders_daily SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mexico_regions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"garmentproduction\")\n", "labels": {"reads": [{"table": "mexico_regions", "columns": null}], "writes": [{"table": "garmentproduction", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ref_locations depends on fieldd_info\ndbt build --select ref_locations --vars 'source: fieldd_info'\n", "labels": {"reads": [{"table": "fieldd_info", "columns": null}], "writes": [{"table": "ref_locations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"carbon_footprint\")\nsrc.write.insertInto(\"bi.bi_inventory_di\", overwrite=True)\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": null}], "writes": [{"table": "bi.bi_inventory_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"socialimpactinvestments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"authenticationlogs\")\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": [{"table": "authenticationlogs", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO movie SELECT a.transaction_date, b.model FROM sustainabilityratings a JOIN high_risk b ON a.mouse_id = b.mouse_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": null}, {"table": "high_risk", "columns": null}], "writes": [{"table": "movie", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart_shipments_full\"\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO architect SELECT trainingid, organic, caseid FROM ocean_floor_mapping WHERE trainingid > 279\"\n", "labels": {"reads": [{"table": "ocean_floor_mapping", "columns": ["trainingid", "organic", "caseid"]}], "writes": [{"table": "architect", "columns": ["trainingid", "organic", "caseid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"producersnewmexico\")\nwrite_to_output(df, \"tv_shows_genre\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": null}], "writes": [{"table": "tv_shows_genre", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tourism SELECT * FROM legacy\ncur.execute(\"SELECT governor, fabricid FROM recyclingrates LIMIT 359\")\n", "labels": {"reads": [{"table": "recyclingrates", "columns": ["governor", "fabricid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO staff_department_assignments SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO faculty_participates_in (average_attendance, devices) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "faculty_participates_in", "columns": ["average_attendance", "devices"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO labor_practices SELECT algorithm, labor_hour_id, extraction_date, city FROM fairness_scores WHERE algorithm > 279\"\n", "labels": {"reads": [{"table": "fairness_scores", "columns": ["algorithm", "labor_hour_id", "extraction_date", "city"]}], "writes": [{"table": "labor_practices", "columns": ["algorithm", "labor_hour_id", "extraction_date", "city"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO feedback SELECT * FROM legacy\ncur.execute(\"SELECT stuid, art_type FROM studies LIMIT 159\")\n", "labels": {"reads": [{"table": "studies", "columns": ["stuid", "art_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_sessions_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"materials_usage\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_hourly", "columns": null}], "writes": [{"table": "materials_usage", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT treatment_date, star_rating_description FROM eventattendance LIMIT 92;\nINSERT INTO exhibitionattendance SELECT programoutcomeid, traveler_id, submission_id FROM arctic_sightings WHERE programoutcomeid > 21;\nSQL\n", "labels": {"reads": [{"table": "eventattendance", "columns": ["treatment_date", "star_rating_description"]}, {"table": "arctic_sightings", "columns": ["programoutcomeid", "traveler_id", "submission_id"]}], "writes": [{"table": "exhibitionattendance", "columns": ["programoutcomeid", "traveler_id", "submission_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT algorithm_name, eventname FROM cargos\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"stores\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cargos", "columns": ["algorithm_name", "eventname"]}], "writes": [{"table": "stores", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_payments_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods.ods_payments_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mental_health_parity SELECT statement_id, system_id, dorm_name FROM soccer_goals WHERE statement_id > 196\"\n", "labels": {"reads": [{"table": "soccer_goals", "columns": ["statement_id", "system_id", "dorm_name"]}], "writes": [{"table": "mental_health_parity", "columns": ["statement_id", "system_id", "dorm_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"book\")\nsrc.write.insertInto(\"hydro_power\", overwrite=True)\n", "labels": {"reads": [{"table": "book", "columns": null}], "writes": [{"table": "hydro_power", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO transportation_fleet (advisor, diet) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "transportation_fleet", "columns": ["advisor", "diet"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_hours\").toPandas()\ndf[[\"inclusive\", \"exhibitioncountry\"]].to_sql(\"dwd.dwd_campaigns_df\", engine, index=False)\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": ["inclusive", "exhibitioncountry"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO rural_development.agriculture_projects SELECT milestone, language FROM courts WHERE milestone > 497\"\n", "labels": {"reads": [{"table": "courts", "columns": ["milestone", "language"]}], "writes": [{"table": "rural_development.agriculture_projects", "columns": ["milestone", "language"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT vice_president_vote, sales_count FROM mart.mart_users_di LIMIT 393\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO artsandcrafts SELECT medium, shelter_id FROM vesselfuel WHERE medium > 353\")\n", "labels": {"reads": [{"table": "mart.mart_users_di", "columns": ["vice_president_vote", "sales_count"]}, {"table": "vesselfuel", "columns": ["medium", "shelter_id"]}], "writes": [{"table": "artsandcrafts", "columns": ["medium", "shelter_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO green_energy_lending_programs SELECT a.completed_course, b.date_to FROM highest_scores a JOIN mailshot_campaigns b ON a.birth_date = b.birth_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "highest_scores", "columns": null}, {"table": "mailshot_campaigns", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO classroom SELECT assets, budgeted FROM properties WHERE assets > 393\"\n", "labels": {"reads": [{"table": "properties", "columns": ["assets", "budgeted"]}], "writes": [{"table": "classroom", "columns": ["assets", "budgeted"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO spacemissions SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO affiliated_with SELECT a.document_name, b.saleamount FROM trends_2022 a JOIN circular_economy_companies b ON a.end_date = b.end_date\"\n", "labels": {"reads": [{"table": "trends_2022", "columns": null}, {"table": "circular_economy_companies", "columns": null}], "writes": [{"table": "affiliated_with", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM stg.stg_coupon_use_hourly\", conn)\ndf.to_sql(\"labor_stats\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": null}], "writes": [{"table": "labor_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO geologicalsurvey SELECT trainingdate, asset_type, avg_depth, dec FROM artwork_styles WHERE trainingdate > 246\"\n", "labels": {"reads": [{"table": "artwork_styles", "columns": ["trainingdate", "asset_type", "avg_depth", "dec"]}], "writes": [{"table": "geologicalsurvey", "columns": ["trainingdate", "asset_type", "avg_depth", "dec"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"latam_schema.education_budget\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM enzyme\", conn)\ndf.to_sql(\"match\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "enzyme", "columns": null}], "writes": [{"table": "match", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO train_lines SELECT issues, date_of_completion, water_temp FROM cargo_tracking WHERE issues > 83;\nEOF\n", "labels": {"reads": [{"table": "cargo_tracking", "columns": ["issues", "date_of_completion", "water_temp"]}], "writes": [{"table": "train_lines", "columns": ["issues", "date_of_completion", "water_temp"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT mine_type, machine_series FROM student_addresses LIMIT 381\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "student_addresses", "columns": ["mine_type", "machine_series"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"soil_moisture\").toPandas()\ndf[[\"product_description\", \"order_shipping_charges\"]].to_sql(\"enzyme\", engine, index=False)\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}], "writes": [{"table": "enzyme", "columns": ["product_description", "order_shipping_charges"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cargo_handling\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"temperatureanomalies\")\n", "labels": {"reads": [{"table": "cargo_handling", "columns": null}], "writes": [{"table": "temperatureanomalies", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT time_hour, cargoid FROM tryout LIMIT 473\")\nrows = cur.fetchall()\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "tryout", "columns": ["time_hour", "cargoid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"shared_rides_tokyo\")\nsrc.write.insertInto(\"water_sources\", overwrite=True)\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": null}], "writes": [{"table": "water_sources", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"therapy_sessions\").toPandas()\ndf[[\"num_developments\", \"order_status_code\"]].to_sql(\"traditionalarts\", engine, index=False)\n", "labels": {"reads": [{"table": "therapy_sessions", "columns": null}], "writes": [{"table": "traditionalarts", "columns": ["num_developments", "order_status_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO contract_transactions SELECT itemname, assets_billion, working_horses, cust_id FROM athlete_wellbeing WHERE itemname > 91\"\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["itemname", "assets_billion", "working_horses", "cust_id"]}], "writes": [{"table": "contract_transactions", "columns": ["itemname", "assets_billion", "working_horses", "cust_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO auto_shows SELECT participated_in_open_pedagogy, date_claim_made, coownerid FROM ratings WHERE participated_in_open_pedagogy > 439\"\n", "labels": {"reads": [{"table": "ratings", "columns": ["participated_in_open_pedagogy", "date_claim_made", "coownerid"]}], "writes": [{"table": "auto_shows", "columns": ["participated_in_open_pedagogy", "date_claim_made", "coownerid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO wildlife SELECT cancel_date, co2_emissions, amount_of_transaction FROM mental_health_parity_violations WHERE cancel_date > 254\")\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": ["cancel_date", "co2_emissions", "amount_of_transaction"]}], "writes": [{"table": "wildlife", "columns": ["cancel_date", "co2_emissions", "amount_of_transaction"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table public.ev_sales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "public.ev_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO electric_buses SELECT depth, crossing, wellname FROM agri_innov WHERE depth > 378\")\n", "labels": {"reads": [{"table": "agri_innov", "columns": ["depth", "crossing", "wellname"]}], "writes": [{"table": "electric_buses", "columns": ["depth", "crossing", "wellname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"healthbudget\")\npush_to_store(df, \"urban_farms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "healthbudget", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table player_sessions --columns vulnerability_name,contract_start_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "player_sessions", "columns": ["vulnerability_name", "contract_start_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"yttrium_production\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"audience\")\n", "labels": {"reads": [{"table": "yttrium_production", "columns": null}], "writes": [{"table": "audience", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT seat_section, date_id FROM mining_operation\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"mental_health_clinics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mining_operation", "columns": ["seat_section", "date_id"]}], "writes": [{"table": "mental_health_clinics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO concert_sales SELECT attendee_age, contract_amount, address_line_2 FROM bi.bi_orders_delta WHERE attendee_age > 452\"\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": ["attendee_age", "contract_amount", "address_line_2"]}], "writes": [{"table": "concert_sales", "columns": ["attendee_age", "contract_amount", "address_line_2"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO galleries SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.vendors_full\").toPandas()\ndf[[\"donor_program\", \"task_id\"]].to_sql(\"country_waste_generation\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.vendors_full", "columns": null}], "writes": [{"table": "country_waste_generation", "columns": ["donor_program", "task_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO industry_funding SELECT dname, animal, milestone, passenger_name FROM marketing_budgets WHERE dname > 137\"\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": ["dname", "animal", "milestone", "passenger_name"]}], "writes": [{"table": "industry_funding", "columns": ["dname", "animal", "milestone", "passenger_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mart_campaigns_delta depends on submersible_dives\ndbt run -s mart_campaigns_delta --vars 'source: submersible_dives'\n", "labels": {"reads": [{"table": "submersible_dives", "columns": null}], "writes": [{"table": "mart_campaigns_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO wedding SELECT document_code, veteran_unemployment_rate, line_id FROM urban_farms WHERE document_code > 169;\nEOF\n", "labels": {"reads": [{"table": "urban_farms", "columns": ["document_code", "veteran_unemployment_rate", "line_id"]}], "writes": [{"table": "wedding", "columns": ["document_code", "veteran_unemployment_rate", "line_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"smart_contracts_table\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "smart_contracts_table", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM african_union_countries\"\n", "labels": {"reads": [{"table": "african_union_countries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO militaryequipment SELECT state_province, union_member FROM program WHERE state_province > 142;\nEOF\n", "labels": {"reads": [{"table": "program", "columns": ["state_province", "union_member"]}], "writes": [{"table": "militaryequipment", "columns": ["state_province", "union_member"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO rooms SELECT plantlocation, artpiecename, mgr_start_date, source_u_id FROM coral_reefs WHERE plantlocation > 275\"], check=True)\n", "labels": {"reads": [{"table": "coral_reefs", "columns": ["plantlocation", "artpiecename", "mgr_start_date", "source_u_id"]}], "writes": [{"table": "rooms", "columns": ["plantlocation", "artpiecename", "mgr_start_date", "source_u_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"news_report\")\nsrc.write.insertInto(\"platformi\", overwrite=True)\n", "labels": {"reads": [{"table": "news_report", "columns": null}], "writes": [{"table": "platformi", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO product_catalog SELECT founding_year, customer_type_code FROM station_emergencies WHERE founding_year > 55\")\n", "labels": {"reads": [{"table": "station_emergencies", "columns": ["founding_year", "customer_type_code"]}], "writes": [{"table": "product_catalog", "columns": ["founding_year", "customer_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shelters SELECT protein_name, recorded_by_staff_id, prof_num FROM stellar_transactions WHERE protein_name > 156\"\n", "labels": {"reads": [{"table": "stellar_transactions", "columns": ["protein_name", "recorded_by_staff_id", "prof_num"]}], "writes": [{"table": "shelters", "columns": ["protein_name", "recorded_by_staff_id", "prof_num"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marine_life_sightings SELECT a.daily_co2_emission, b.browser_id FROM deep_sea_expeditions a JOIN department_store_chain b ON a.served_subscribers = b.served_subscribers\"\n", "labels": {"reads": [{"table": "deep_sea_expeditions", "columns": null}, {"table": "department_store_chain", "columns": null}], "writes": [{"table": "marine_life_sightings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nsql = \"INSERT INTO public.police_calls SELECT a.hiredate, b.sustainability_score FROM disease_prevalence a JOIN wind_farms b ON a.vaccine_name = b.vaccine_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}, {"table": "wind_farms", "columns": null}], "writes": [{"table": "public.police_calls", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT publisher, form_id FROM militarycyberops LIMIT 340;\nINSERT INTO yttrium_production SELECT bill_id, emp_lname, prod_id, delivery_status FROM view_product_availability WHERE bill_id > 422;\nSQL\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["publisher", "form_id"]}, {"table": "view_product_availability", "columns": ["bill_id", "emp_lname", "prod_id", "delivery_status"]}], "writes": [{"table": "yttrium_production", "columns": ["bill_id", "emp_lname", "prod_id", "delivery_status"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO underwater_cables SELECT total_shipped, crop, training_id, characteristic_type_code FROM artistsdemographics WHERE total_shipped > 457\"\n", "labels": {"reads": [{"table": "artistsdemographics", "columns": ["total_shipped", "crop", "training_id", "characteristic_type_code"]}], "writes": [{"table": "underwater_cables", "columns": ["total_shipped", "crop", "training_id", "characteristic_type_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT diversity_score, meal_date FROM grad_students LIMIT 61\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO chemicals_annual SELECT num_workers, rig_name, incidentdate FROM category_revenue WHERE num_workers > 317\")\n", "labels": {"reads": [{"table": "grad_students", "columns": ["diversity_score", "meal_date"]}, {"table": "category_revenue", "columns": ["num_workers", "rig_name", "incidentdate"]}], "writes": [{"table": "chemicals_annual", "columns": ["num_workers", "rig_name", "incidentdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart_campaigns_delta SELECT * FROM legacy\ncur.execute(\"SELECT location_text, min_age FROM scan_dates LIMIT 15\")\n", "labels": {"reads": [{"table": "scan_dates", "columns": ["location_text", "min_age"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model audience_demographics depends on film_actor\ndbt build --select audience_demographics --vars 'source: film_actor'\n", "labels": {"reads": [{"table": "film_actor", "columns": null}], "writes": [{"table": "audience_demographics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cultural_diversity, site_name FROM office_locations\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"ads.events\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "office_locations", "columns": ["cultural_diversity", "site_name"]}], "writes": [{"table": "ads.events", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT threat, preferred_foot FROM az_drought_impact LIMIT 457;\nINSERT INTO cotton_source SELECT lastclaimdate, passengers, hometeamid FROM neighborhoods WHERE lastclaimdate > 206;\nSQL\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": ["threat", "preferred_foot"]}, {"table": "neighborhoods", "columns": ["lastclaimdate", "passengers", "hometeamid"]}], "writes": [{"table": "cotton_source", "columns": ["lastclaimdate", "passengers", "hometeamid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artworksales (employment_id, warehousename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artworksales", "columns": ["employment_id", "warehousename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO crimes SELECT attendance_date, organizationname, charging_level, launch_company FROM recycled_polyester WHERE attendance_date > 188\"], check=True)\n", "labels": {"reads": [{"table": "recycled_polyester", "columns": ["attendance_date", "organizationname", "charging_level", "launch_company"]}], "writes": [{"table": "crimes", "columns": ["attendance_date", "organizationname", "charging_level", "launch_company"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table shared_ebikes --columns wildlife_type_id,strain_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "shared_ebikes", "columns": ["wildlife_type_id", "strain_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO rural_clinics SELECT gtype, mission_name, energytype, international_passengers FROM stations WHERE gtype > 197;\nEOF\n", "labels": {"reads": [{"table": "stations", "columns": ["gtype", "mission_name", "energytype", "international_passengers"]}], "writes": [{"table": "rural_clinics", "columns": ["gtype", "mission_name", "energytype", "international_passengers"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"runs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO musicsales SELECT has_disability, departmentname FROM ratings WHERE has_disability > 259\"\n", "labels": {"reads": [{"table": "ratings", "columns": ["has_disability", "departmentname"]}], "writes": [{"table": "musicsales", "columns": ["has_disability", "departmentname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT trip_start_time, studio FROM film LIMIT 93\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO mart.clicks SELECT date_of_transaction, apt_type_code FROM classrooms WHERE date_of_transaction > 218\")\n", "labels": {"reads": [{"table": "film", "columns": ["trip_start_time", "studio"]}, {"table": "classrooms", "columns": ["date_of_transaction", "apt_type_code"]}], "writes": [{"table": "mart.clicks", "columns": ["date_of_transaction", "apt_type_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table fleets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fleets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"disasters\")\nsrc.write.insertInto(\"mart.mart_coupon_use_full\", overwrite=True)\n", "labels": {"reads": [{"table": "disasters", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mentalhealthprofessional SELECT a.winning_pilot, b.restaurantname FROM tree_types a JOIN bi.users_df b ON a.ingredient = b.ingredient\"\n", "labels": {"reads": [{"table": "tree_types", "columns": null}, {"table": "bi.users_df", "columns": null}], "writes": [{"table": "mentalhealthprofessional", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO player_attributes SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT nation, dockingdate FROM chargingstations LIMIT 478;\nINSERT INTO runs SELECT drug, parent_organization_id FROM view_unit_status WHERE drug > 473;\nSQL\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["nation", "dockingdate"]}, {"table": "view_unit_status", "columns": ["drug", "parent_organization_id"]}], "writes": [{"table": "runs", "columns": ["drug", "parent_organization_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM athlete_wellbeing\", conn)\ndf.to_sql(\"videos\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": null}], "writes": [{"table": "videos", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO person SELECT a.award_date, b.problem_id FROM judges a JOIN dallas_fire_incidents b ON a.site_id = b.site_id\"\n", "labels": {"reads": [{"table": "judges", "columns": null}, {"table": "dallas_fire_incidents", "columns": null}], "writes": [{"table": "person", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_payments_full SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO taj_mahal_visitors SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO course (thefttypeid, therapy_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "course", "columns": ["thefttypeid", "therapy_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_farms\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"textile_sourcing\")\n", "labels": {"reads": [{"table": "urban_farms", "columns": null}], "writes": [{"table": "textile_sourcing", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO astronauts SELECT a.training_id, b.sculpture_name FROM initiatives_3 a JOIN marathons b ON a.habitat_id = b.habitat_id\"\n", "labels": {"reads": [{"table": "initiatives_3", "columns": null}, {"table": "marathons", "columns": null}], "writes": [{"table": "astronauts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"euro_champs_track_field\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"innovation_metrics\")\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": null}], "writes": [{"table": "innovation_metrics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"state_contracts\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "state_contracts", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO broadband_revenue SELECT author, team_id_winner, state_province FROM state_info WHERE author > 452;\nEOF\n", "labels": {"reads": [{"table": "state_info", "columns": ["author", "team_id_winner", "state_province"]}], "writes": [{"table": "broadband_revenue", "columns": ["author", "team_id_winner", "state_province"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attorney_last_name, event_id FROM employee\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"digital_trends\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "employee", "columns": ["attorney_last_name", "event_id"]}], "writes": [{"table": "digital_trends", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model renewable_energy_projects depends on bridgeconstruction\ndbt run -s renewable_energy_projects --vars '{\"source_table\":\"bridgeconstruction\"}'\n", "labels": {"reads": [{"table": "bridgeconstruction", "columns": null}], "writes": [{"table": "renewable_energy_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.refunds SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.clicks_delta SELECT a.oil_volume, b.engagement_date FROM country_labor a JOIN driver b ON a.phase = b.phase\"\n", "labels": {"reads": [{"table": "country_labor", "columns": null}, {"table": "driver", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_risk_score SELECT platform_id, tournament_name FROM dw_member_point_full WHERE platform_id > 173\"], check=True)\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": ["platform_id", "tournament_name"]}], "writes": [{"table": "stg.stg_risk_score", "columns": ["platform_id", "tournament_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO influencers SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nailpolishsales SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dates\")\nsrc.write.insertInto(\"militarybases\", overwrite=True)\n", "labels": {"reads": [{"table": "dates", "columns": null}], "writes": [{"table": "militarybases", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO midwest_region (itemid, teacher_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "midwest_region", "columns": ["itemid", "teacher_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO team_franchise SELECT district_id, developer FROM traditional_arts WHERE district_id > 178;\nEOF\n", "labels": {"reads": [{"table": "traditional_arts", "columns": ["district_id", "developer"]}], "writes": [{"table": "team_franchise", "columns": ["district_id", "developer"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO animal_rehab SELECT game, strat_name, height_feet, pid FROM properties WHERE game > 241\"\n", "labels": {"reads": [{"table": "properties", "columns": ["game", "strat_name", "height_feet", "pid"]}], "writes": [{"table": "animal_rehab", "columns": ["game", "strat_name", "height_feet", "pid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO territory.human_rights_data (star_rating_code, donor_program) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "territory.human_rights_data", "columns": ["star_rating_code", "donor_program"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO survey_data (train_id, mental_health_score) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "survey_data", "columns": ["train_id", "mental_health_score"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"dws.dws_coupon_use_full\")\nexport_to_output(df, \"review\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_full", "columns": null}], "writes": [{"table": "review", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"miningdepartment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "miningdepartment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table machinery --columns grant_id,donationamount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "machinery", "columns": ["grant_id", "donationamount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM menu_vendors\", conn)\ndf.to_sql(\"militarypersonnel\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "menu_vendors", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.environmental_impact > 217).all()\n# src table: disaster_zones\nengine.execute(\"INSERT INTO dp_articles SELECT * FROM disaster_zones\")\n", "labels": {"reads": [{"table": "disaster_zones", "columns": null}], "writes": [{"table": "dp_articles", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO accelerator_compatible_browser SELECT fastestlapspeed, campaign_id, screening FROM co2emissions WHERE fastestlapspeed > 429;\nEOF\n", "labels": {"reads": [{"table": "co2emissions", "columns": ["fastestlapspeed", "campaign_id", "screening"]}], "writes": [{"table": "accelerator_compatible_browser", "columns": ["fastestlapspeed", "campaign_id", "screening"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO assets_frameworks SELECT framework_name, exploited, image_date FROM district_schools WHERE framework_name > 498\"], check=True)\n", "labels": {"reads": [{"table": "district_schools", "columns": ["framework_name", "exploited", "image_date"]}], "writes": [{"table": "assets_frameworks", "columns": ["framework_name", "exploited", "image_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model player depends on yttrium_production\ndbt build --select player --vars 'source: yttrium_production'\n", "labels": {"reads": [{"table": "yttrium_production", "columns": null}], "writes": [{"table": "player", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.added_date > 233).all()\n# src table: soccer_teams\nengine.execute(\"INSERT INTO vr_tech SELECT * FROM soccer_teams\")\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}], "writes": [{"table": "vr_tech", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO trade_history SELECT num_attendees, import_country FROM accessibility_audits WHERE num_attendees > 350\"\n", "labels": {"reads": [{"table": "accessibility_audits", "columns": ["num_attendees", "import_country"]}], "writes": [{"table": "trade_history", "columns": ["num_attendees", "import_country"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO ocean_shipping.cargo SELECT a.clinic_id, b.customer_number FROM seeds a JOIN fair_trade_brands b ON a.airline = b.airline\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "seeds", "columns": null}, {"table": "fair_trade_brands", "columns": null}], "writes": [{"table": "ocean_shipping.cargo", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dwd_risk_score_delta depends on communication_scores\ndbt build --select dwd_risk_score_delta --vars '{\"source_table\":\"communication_scores\"}'\n", "labels": {"reads": [{"table": "communication_scores", "columns": null}], "writes": [{"table": "dwd_risk_score_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbon_prices_3\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"africa_projects\")\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": null}], "writes": [{"table": "africa_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"creative_ai\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "creative_ai", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT capacity_mw, implemented_date FROM storage LIMIT 145\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO ads.orders SELECT taskid, master_customer_id, leader, is_accessible FROM investment_accounts WHERE taskid > 194\")\n", "labels": {"reads": [{"table": "storage", "columns": ["capacity_mw", "implemented_date"]}, {"table": "investment_accounts", "columns": ["taskid", "master_customer_id", "leader", "is_accessible"]}], "writes": [{"table": "ads.orders", "columns": ["taskid", "master_customer_id", "leader", "is_accessible"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO labor_cost SELECT productid, financially_capable, decoration_theme, oil_production_q4_2021 FROM ai_papers WHERE productid > 416\"\n", "labels": {"reads": [{"table": "ai_papers", "columns": ["productid", "financially_capable", "decoration_theme", "oil_production_q4_2021"]}], "writes": [{"table": "labor_cost", "columns": ["productid", "financially_capable", "decoration_theme", "oil_production_q4_2021"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ref_hotel_star_ratings\"\n", "labels": {"reads": [{"table": "ref_hotel_star_ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO projecttimelinebybudget SELECT a.installed_date, b.customer_details FROM community_health_center a JOIN auto_shows b ON a.document_structure_code = b.document_structure_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "community_health_center", "columns": null}, {"table": "auto_shows", "columns": null}], "writes": [{"table": "projecttimelinebybudget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM defense_diplomacy\", conn)\ndf.to_sql(\"dw.clicks_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": null}], "writes": [{"table": "dw.clicks_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"higher_ed.publications\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO art_pieces SELECT a.provider, b.bank_id FROM stg.orders_daily a JOIN workout_data b ON a.materialid = b.materialid\"\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": null}, {"table": "workout_data", "columns": null}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO platformi (violationtype, is_ev) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "platformi", "columns": ["violationtype", "is_ev"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT energy_efficiency_rating, complaintid FROM wastewater_facilities LIMIT 78\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO airport_aircraft SELECT energy_generated, supplierid, manufacturer_id FROM ca_menu_items WHERE energy_generated > 12\")\n", "labels": {"reads": [{"table": "wastewater_facilities", "columns": ["energy_efficiency_rating", "complaintid"]}, {"table": "ca_menu_items", "columns": ["energy_generated", "supplierid", "manufacturer_id"]}], "writes": [{"table": "airport_aircraft", "columns": ["energy_generated", "supplierid", "manufacturer_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT min_depth, mean_temperature_f FROM call_volume LIMIT 9\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ticketsales SELECT org_id, physical, market_id FROM cybersecurity_incidents WHERE org_id > 255\")\n", "labels": {"reads": [{"table": "call_volume", "columns": ["min_depth", "mean_temperature_f"]}, {"table": "cybersecurity_incidents", "columns": ["org_id", "physical", "market_id"]}], "writes": [{"table": "ticketsales", "columns": ["org_id", "physical", "market_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_records\").toPandas()\ndf[[\"offset_id\", \"cultural_competency_score\"]].to_sql(\"ethics_violations\", engine, index=False)\n", "labels": {"reads": [{"table": "safety_records", "columns": null}], "writes": [{"table": "ethics_violations", "columns": ["offset_id", "cultural_competency_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM genetic.projects\"\n", "labels": {"reads": [{"table": "genetic.projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table ocean_depths --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ocean_depths", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO communityhealthworkers (sustainability_initiative_id, order_status) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "communityhealthworkers", "columns": ["sustainability_initiative_id", "order_status"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO view_product_availability SELECT taskid, vaccination_status, personnel_id FROM dorm WHERE taskid > 247\")\n", "labels": {"reads": [{"table": "dorm", "columns": ["taskid", "vaccination_status", "personnel_id"]}], "writes": [{"table": "view_product_availability", "columns": ["taskid", "vaccination_status", "personnel_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tree_species SELECT * FROM legacy\ncur.execute(\"SELECT volunteer_hours, game_genre FROM mart.shipments_full LIMIT 159\")\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["volunteer_hours", "game_genre"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model appointment depends on nba_games\ndbt run --models appointment --vars '{\"source_table\":\"nba_games\"}'\n", "labels": {"reads": [{"table": "nba_games", "columns": null}], "writes": [{"table": "appointment", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO gene SELECT branch_id, reader_id, healthequitymetricscore, field_name FROM green_buildings_us WHERE branch_id > 88\"\n", "labels": {"reads": [{"table": "green_buildings_us", "columns": ["branch_id", "reader_id", "healthequitymetricscore", "field_name"]}], "writes": [{"table": "gene", "columns": ["branch_id", "reader_id", "healthequitymetricscore", "field_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO policy SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_sales\").toPandas()\ndf[[\"bank_name\", \"star_rating_code\"]].to_sql(\"student_course_registrations\", engine, index=False)\n", "labels": {"reads": [{"table": "military_sales", "columns": null}], "writes": [{"table": "student_course_registrations", "columns": ["bank_name", "star_rating_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nsql = \"INSERT INTO dws.dws_refunds_hourly SELECT a.residence, b.products_this_year FROM customersregion a JOIN bi_shipments_daily b ON a.energytype = b.energytype\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "customersregion", "columns": null}, {"table": "bi_shipments_daily", "columns": null}], "writes": [{"table": "dws.dws_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT half, instructor_id FROM organicproducts\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"marine_life_research_stations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "organicproducts", "columns": ["half", "instructor_id"]}], "writes": [{"table": "marine_life_research_stations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"performers\")\nsrc.write.insertInto(\"textileworkers\", overwrite=True)\n", "labels": {"reads": [{"table": "performers", "columns": null}], "writes": [{"table": "textileworkers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT player_id, production_value FROM ods.ods_risk_score_df LIMIT 497\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": ["player_id", "production_value"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ratings\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ratings", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model founders depends on dwd.dwd_vendors\ndbt run -s founders --vars '{\"source_table\":\"dwd.dwd_vendors\"}'\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": null}], "writes": [{"table": "founders", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO government_transparency (price, account_number) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "government_transparency", "columns": ["price", "account_number"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"artist\")\nexport_to_sink(df, \"appliances\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "artist", "columns": null}], "writes": [{"table": "appliances", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT co2_offset_amount, organization_name FROM royal_family LIMIT 222\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "royal_family", "columns": ["co2_offset_amount", "organization_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model warehouses depends on winter_olympics\ndbt run -s warehouses --vars '{\"source_table\":\"winter_olympics\"}'\n", "labels": {"reads": [{"table": "winter_olympics", "columns": null}], "writes": [{"table": "warehouses", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO ref_detention_type SELECT dname, rating_in_percent, ei_category, investment_round FROM marine_life_data WHERE dname > 135\"\n", "labels": {"reads": [{"table": "marine_life_data", "columns": ["dname", "rating_in_percent", "ei_category", "investment_round"]}], "writes": [{"table": "ref_detention_type", "columns": ["dname", "rating_in_percent", "ei_category", "investment_round"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table flights --columns participated_in_open_pedagogy,dormid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "flights", "columns": ["participated_in_open_pedagogy", "dormid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model grapes depends on community.donations\ndbt build -s grapes --vars '{\"src\":\"community.donations\"}'\n", "labels": {"reads": [{"table": "community.donations", "columns": null}], "writes": [{"table": "grapes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.component_name > 422).all()\n# src table: measurements\nengine.execute(\"INSERT INTO properties SELECT * FROM measurements\")\n", "labels": {"reads": [{"table": "measurements", "columns": null}], "writes": [{"table": "properties", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_inventory_hourly SELECT a.startyear, b.assistingnurse FROM electricvehicleadoption a JOIN organization b ON a.fundingdate = b.fundingdate\"\n", "labels": {"reads": [{"table": "electricvehicleadoption", "columns": null}, {"table": "organization", "columns": null}], "writes": [{"table": "stg.stg_inventory_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model intelligence_agency depends on employee_demographics\ndbt build -s intelligence_agency --vars '{\"source_table\":\"employee_demographics\"}'\n", "labels": {"reads": [{"table": "employee_demographics", "columns": null}], "writes": [{"table": "intelligence_agency", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT conferenceid, program_expenses FROM support_programs LIMIT 498\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO bi.bi_payments_delta SELECT publicationid, mediatypeid, event_details, marketing_region_name FROM production_yearly WHERE publicationid > 51\")\n", "labels": {"reads": [{"table": "support_programs", "columns": ["conferenceid", "program_expenses"]}, {"table": "production_yearly", "columns": ["publicationid", "mediatypeid", "event_details", "marketing_region_name"]}], "writes": [{"table": "bi.bi_payments_delta", "columns": ["publicationid", "mediatypeid", "event_details", "marketing_region_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model editor depends on gamedesigndata\ndbt run -s editor --vars 'source: gamedesigndata'\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": [{"table": "editor", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO garmentproduction SELECT * FROM legacy\ncur.execute(\"SELECT policy_type, dispensary_id FROM causes LIMIT 392\")\n", "labels": {"reads": [{"table": "causes", "columns": ["policy_type", "dispensary_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.ads_exposure_di SELECT * FROM legacy\ncur.execute(\"SELECT funding_id, fairness_score FROM ref_detention_type LIMIT 327\")\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": ["funding_id", "fairness_score"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"all_star\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_orders\")\n", "labels": {"reads": [{"table": "all_star", "columns": null}], "writes": [{"table": "ads.ads_orders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM genetics.experiments\", conn)\ndf.to_sql(\"astronaut_missions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": null}], "writes": [{"table": "astronaut_missions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO factory_workers SELECT hospital_name, stageposition, staystart FROM claims_documents WHERE hospital_name > 221\"\n", "labels": {"reads": [{"table": "claims_documents", "columns": ["hospital_name", "stageposition", "staystart"]}], "writes": [{"table": "factory_workers", "columns": ["hospital_name", "stageposition", "staystart"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO warehouses SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO artistsdemographics SELECT scan_date, manufacturerid FROM resilience_infrastructure WHERE scan_date > 449\"\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": ["scan_date", "manufacturerid"]}], "writes": [{"table": "artistsdemographics", "columns": ["scan_date", "manufacturerid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO legal_aid_providers SELECT a.branch, b.prof_office FROM bi_device_log_daily a JOIN africa_schema.african_mines b ON a.card_type_code = b.card_type_code\"\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": null}, {"table": "africa_schema.african_mines", "columns": null}], "writes": [{"table": "legal_aid_providers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.inventory_df SELECT emp_jobcode, preference_rating, visitors, hours_contributed FROM virtual_tours WHERE emp_jobcode > 393\"\n", "labels": {"reads": [{"table": "virtual_tours", "columns": ["emp_jobcode", "preference_rating", "visitors", "hours_contributed"]}], "writes": [{"table": "ods.inventory_df", "columns": ["emp_jobcode", "preference_rating", "visitors", "hours_contributed"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO rare_earth_companies SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"project_timelines\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "project_timelines", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"third_party_companies\")\nsrc.write.insertInto(\"all_programs\", overwrite=True)\n", "labels": {"reads": [{"table": "third_party_companies", "columns": null}], "writes": [{"table": "all_programs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi_products\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"paris_train\")\n", "labels": {"reads": [{"table": "bi_products", "columns": null}], "writes": [{"table": "paris_train", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenance_requests\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"daily_oil_production\")\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}], "writes": [{"table": "daily_oil_production", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT founder_count, high_estimate FROM restorative_justice_3 LIMIT 377;\nINSERT INTO music_festival SELECT wheels, role_code, spill_name FROM worker_scores WHERE wheels > 186;\nSQL\n", "labels": {"reads": [{"table": "restorative_justice_3", "columns": ["founder_count", "high_estimate"]}, {"table": "worker_scores", "columns": ["wheels", "role_code", "spill_name"]}], "writes": [{"table": "music_festival", "columns": ["wheels", "role_code", "spill_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table hr.employees --columns rating,num_of_component --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "hr.employees", "columns": ["rating", "num_of_component"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO climateresearch SELECT product_price, home_team_points FROM country_landfill_capacity WHERE product_price > 303\"\n", "labels": {"reads": [{"table": "country_landfill_capacity", "columns": ["product_price", "home_team_points"]}], "writes": [{"table": "climateresearch", "columns": ["product_price", "home_team_points"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_energy_projects\").toPandas()\ndf[[\"bus_id\", \"citizen_id\"]].to_sql(\"staff_roles\", engine, index=False)\n", "labels": {"reads": [{"table": "renewable_energy_projects", "columns": null}], "writes": [{"table": "staff_roles", "columns": ["bus_id", "citizen_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO pets (impressions, project_details) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "pets", "columns": ["impressions", "project_details"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fish_biomass SELECT preferred_foot, testtypeid, gender_code FROM public.forest_stats WHERE preferred_foot > 175\"\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": ["preferred_foot", "testtypeid", "gender_code"]}], "writes": [{"table": "fish_biomass", "columns": ["preferred_foot", "testtypeid", "gender_code"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO networkdevices SELECT co2_offset_amount, hireyear FROM broadband_subscribers WHERE co2_offset_amount > 281\"], check=True)\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": ["co2_offset_amount", "hireyear"]}], "writes": [{"table": "networkdevices", "columns": ["co2_offset_amount", "hireyear"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT statename, emp_dob FROM eventparticipation\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"labor_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "eventparticipation", "columns": ["statename", "emp_dob"]}], "writes": [{"table": "labor_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_warehouses\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ads_orders depends on restorative_justice_programs\ndbt run -s ads_orders --vars 'source: restorative_justice_programs'\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": null}], "writes": [{"table": "ads_orders", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO locations SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT donor_country, excavation_site_id FROM bioprocesses LIMIT 479\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO eu_data_usage SELECT albumname, checkout FROM judges WHERE albumname > 192\")\n", "labels": {"reads": [{"table": "bioprocesses", "columns": ["donor_country", "excavation_site_id"]}, {"table": "judges", "columns": ["albumname", "checkout"]}], "writes": [{"table": "eu_data_usage", "columns": ["albumname", "checkout"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO viewership SELECT runtime, project FROM sustainable_fabrics WHERE runtime > 308\"\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": ["runtime", "project"]}], "writes": [{"table": "viewership", "columns": ["runtime", "project"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO exoplanet_discoveries (shop_details, starttime) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "exoplanet_discoveries", "columns": ["shop_details", "starttime"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table state_energy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "state_energy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"digital_assets\")\nsrc.write.insertInto(\"fare_collection\", overwrite=True)\n", "labels": {"reads": [{"table": "digital_assets", "columns": null}], "writes": [{"table": "fare_collection", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO customersregion SELECT loan_id, aid_name, focus, employeeid FROM regular_order_products WHERE loan_id > 372\"\n", "labels": {"reads": [{"table": "regular_order_products", "columns": ["loan_id", "aid_name", "focus", "employeeid"]}], "writes": [{"table": "customersregion", "columns": ["loan_id", "aid_name", "focus", "employeeid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"shipment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "shipment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.role_code > 284).all()\n# src table: regulatoryframeworksbycountry\nengine.execute(\"INSERT INTO healthcare_centers SELECT * FROM regulatoryframeworksbycountry\")\n", "labels": {"reads": [{"table": "regulatoryframeworksbycountry", "columns": null}], "writes": [{"table": "healthcare_centers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model esports_teams depends on missions\ndbt run --select esports_teams --vars '{\"src\":\"missions\"}'\n", "labels": {"reads": [{"table": "missions", "columns": null}], "writes": [{"table": "esports_teams", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT loadingend, copy_number FROM gamedata LIMIT 371\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO lots SELECT bill_id, exhibitionname, stu_hrs, journal_id FROM daily_industrial_water_usage WHERE bill_id > 52\")\n", "labels": {"reads": [{"table": "gamedata", "columns": ["loadingend", "copy_number"]}, {"table": "daily_industrial_water_usage", "columns": ["bill_id", "exhibitionname", "stu_hrs", "journal_id"]}], "writes": [{"table": "lots", "columns": ["bill_id", "exhibitionname", "stu_hrs", "journal_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.sessions_full SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_events_daily SELECT do_value, area_sqkm, cases_count FROM customersregion WHERE do_value > 83\"\n", "labels": {"reads": [{"table": "customersregion", "columns": ["do_value", "area_sqkm", "cases_count"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["do_value", "area_sqkm", "cases_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"staff\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "staff", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"player_coach\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "player_coach", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO international_visitors SELECT inventoryid, target_id, claim_id FROM device_usage WHERE inventoryid > 80\"\n", "labels": {"reads": [{"table": "device_usage", "columns": ["inventoryid", "target_id", "claim_id"]}], "writes": [{"table": "international_visitors", "columns": ["inventoryid", "target_id", "claim_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_inventory_delta --columns subject_name,energy_production --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory_delta", "columns": ["subject_name", "energy_production"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model document_locations depends on safety_incidents_india\ndbt run --models document_locations --vars '{\"source_table\":\"safety_incidents_india\"}'\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": null}], "writes": [{"table": "document_locations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table certifications --columns vaccine_type,products_this_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "certifications", "columns": ["vaccine_type", "products_this_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"agencies\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"student_course_registrations\")\n", "labels": {"reads": [{"table": "agencies", "columns": null}], "writes": [{"table": "student_course_registrations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model communityhealthworkers depends on device\ndbt build --models communityhealthworkers --vars '{\"source_table\":\"device\"}'\n", "labels": {"reads": [{"table": "device", "columns": null}], "writes": [{"table": "communityhealthworkers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trucks SELECT bioprocess_id, has_spf, city_id, inventory_id FROM waterconservationbudget WHERE bioprocess_id > 473\"\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": ["bioprocess_id", "has_spf", "city_id", "inventory_id"]}], "writes": [{"table": "trucks", "columns": ["bioprocess_id", "has_spf", "city_id", "inventory_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sports\"\n", "labels": {"reads": [{"table": "sports", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO innovation_trends SELECT a.emp_num, b.instructor FROM otas a JOIN emissions b ON a.sale_price = b.sale_price\"\n", "labels": {"reads": [{"table": "otas", "columns": null}, {"table": "emissions", "columns": null}], "writes": [{"table": "innovation_trends", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO yttrium_production SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"orgdonations\")\nsrc.write.insertInto(\"makeup_products\", overwrite=True)\n", "labels": {"reads": [{"table": "orgdonations", "columns": null}], "writes": [{"table": "makeup_products", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO onlineengagement SELECT visit_date, ad_type FROM vehicle_counts WHERE visit_date > 254\"\n", "labels": {"reads": [{"table": "vehicle_counts", "columns": ["visit_date", "ad_type"]}], "writes": [{"table": "onlineengagement", "columns": ["visit_date", "ad_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table recyclingprogram --target-dir /tmp/land\n", "labels": {"reads": [{"table": "recyclingprogram", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vehicle_data (pid, crop) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vehicle_data", "columns": ["pid", "crop"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO player_attributes SELECT focal_length_mm, resource_id, reporter_id, major FROM ais WHERE focal_length_mm > 2\"], check=True)\n", "labels": {"reads": [{"table": "ais", "columns": ["focal_length_mm", "resource_id", "reporter_id", "major"]}], "writes": [{"table": "player_attributes", "columns": ["focal_length_mm", "resource_id", "reporter_id", "major"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO wastewater_facilities SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"otas\").toPandas()\ndf[[\"trial_id\", \"architect_id\"]].to_sql(\"organization\", engine, index=False)\n", "labels": {"reads": [{"table": "otas", "columns": null}], "writes": [{"table": "organization", "columns": ["trial_id", "architect_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO conservation_programs SELECT a.wins, b.excavationid FROM deep_sea_expeditions a JOIN reporters b ON a.material_id = b.material_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "deep_sea_expeditions", "columns": null}, {"table": "reporters", "columns": null}], "writes": [{"table": "conservation_programs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT athlete_id, incident_type_code FROM workforce_development_programs LIMIT 222\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO pipelines_us_canada SELECT stat_id, shipped_to FROM plants WHERE stat_id > 137\")\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": ["athlete_id", "incident_type_code"]}, {"table": "plants", "columns": ["stat_id", "shipped_to"]}], "writes": [{"table": "pipelines_us_canada", "columns": ["stat_id", "shipped_to"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"course_authors_and_tutors\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"collections\")\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": null}], "writes": [{"table": "collections", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO sustainability_initiatives SELECT a.vote_percent, b.ship_agent_id FROM fish_biomass a JOIN artsales b ON a.ei_category = b.ei_category\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fish_biomass", "columns": null}, {"table": "artsales", "columns": null}], "writes": [{"table": "sustainability_initiatives", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table space_missions_2 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "space_missions_2", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO crime_reports SELECT haslegalprecedent, budgeted FROM humanitarian_operations WHERE haslegalprecedent > 460\"], check=True)\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": ["haslegalprecedent", "budgeted"]}], "writes": [{"table": "crime_reports", "columns": ["haslegalprecedent", "budgeted"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.volunteer_quarter > 161).all()\n# src table: bi_refunds_daily\nengine.execute(\"INSERT INTO discount_coupons SELECT * FROM bi_refunds_daily\")\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": null}], "writes": [{"table": "discount_coupons", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT drug_id, employment_id FROM marketing_regions LIMIT 405\")\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO sports SELECT program_category, total_budget_percent_budgeted, vessel_id FROM arcticwildlifereserve WHERE program_category > 494\")\n", "labels": {"reads": [{"table": "marketing_regions", "columns": ["drug_id", "employment_id"]}, {"table": "arcticwildlifereserve", "columns": ["program_category", "total_budget_percent_budgeted", "vessel_id"]}], "writes": [{"table": "sports", "columns": ["program_category", "total_budget_percent_budgeted", "vessel_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT archaeologist_id, warehousename FROM english_premier_league LIMIT 91;\nINSERT INTO bi.bi_vendors_di SELECT reports_to, lot_id, noise_level, spacecraft_model FROM gamesales WHERE reports_to > 304;\nSQL\n", "labels": {"reads": [{"table": "english_premier_league", "columns": ["archaeologist_id", "warehousename"]}, {"table": "gamesales", "columns": ["reports_to", "lot_id", "noise_level", "spacecraft_model"]}], "writes": [{"table": "bi.bi_vendors_di", "columns": ["reports_to", "lot_id", "noise_level", "spacecraft_model"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tryout\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_education_programs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "community_education_programs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO restorative_justice_3 SELECT * FROM legacy\ncur.execute(\"SELECT primary, mean_sea_level_pressure_inches FROM club LIMIT 135\")\n", "labels": {"reads": [{"table": "club", "columns": ["primary", "mean_sea_level_pressure_inches"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO therapy_attendance SELECT worker_count, average_age, union_member FROM traffic_violations WHERE worker_count > 117\"\n", "labels": {"reads": [{"table": "traffic_violations", "columns": ["worker_count", "average_age", "union_member"]}], "writes": [{"table": "therapy_attendance", "columns": ["worker_count", "average_age", "union_member"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO scores SELECT a.workoutdate, b.launch_date FROM engineer_skills a JOIN tracklists b ON a.ll_activity = b.ll_activity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "engineer_skills", "columns": null}, {"table": "tracklists", "columns": null}], "writes": [{"table": "scores", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO classrooms SELECT issued_date, medical_professional_id, dept_store_chain_id, exploited FROM bus_fares WHERE issued_date > 22\"\n", "labels": {"reads": [{"table": "bus_fares", "columns": ["issued_date", "medical_professional_id", "dept_store_chain_id", "exploited"]}], "writes": [{"table": "classrooms", "columns": ["issued_date", "medical_professional_id", "dept_store_chain_id", "exploited"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model freightforwarding depends on carbon_prices_3\ndbt build -s freightforwarding --vars '{\"src\":\"carbon_prices_3\"}'\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": null}], "writes": [{"table": "freightforwarding", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nresult = value * ratio + offset\nsql = \"INSERT INTO intelligence_personnel SELECT a.customer_first_name, b.opname FROM recovery_program a JOIN atlantic_marine_life b ON a.head_id = b.head_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recovery_program", "columns": null}, {"table": "atlantic_marine_life", "columns": null}], "writes": [{"table": "intelligence_personnel", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO phishing_targets SELECT mine_name, energy_generated, fair_trade, institution FROM students_lifelong_learning WHERE mine_name > 110\"\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["mine_name", "energy_generated", "fair_trade", "institution"]}], "writes": [{"table": "phishing_targets", "columns": ["mine_name", "energy_generated", "fair_trade", "institution"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO restaurant SELECT implementation_year, artist FROM dwd.dwd_orders_daily WHERE implementation_year > 480\"\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": ["implementation_year", "artist"]}], "writes": [{"table": "restaurant", "columns": ["implementation_year", "artist"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fish_population, worker_id FROM wta_serves LIMIT 370\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "wta_serves", "columns": ["fish_population", "worker_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fieldid, support_id FROM communitypolicing LIMIT 151\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "communitypolicing", "columns": ["fieldid", "support_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"skincareinventory\")\nupsert_to_sink(df, \"check_ins\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "skincareinventory", "columns": null}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO inclusive_housing SELECT cases, streams FROM government.region WHERE cases > 475\"\n", "labels": {"reads": [{"table": "government.region", "columns": ["cases", "streams"]}], "writes": [{"table": "inclusive_housing", "columns": ["cases", "streams"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT calendar, archaeologist_id FROM dws_clicks_di\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"iron\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": ["calendar", "archaeologist_id"]}], "writes": [{"table": "iron", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table ads --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO lenders SELECT a.awayteamid, b.fan_id FROM branch a JOIN miningwaterusage b ON a.project_name = b.project_name\"\n", "labels": {"reads": [{"table": "branch", "columns": null}, {"table": "miningwaterusage", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT song_id, dphone FROM seal_population LIMIT 179\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "seal_population", "columns": ["song_id", "dphone"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"budget_allocations\").toPandas()\ndf[[\"chemical_id\", \"prof_office\"]].to_sql(\"mart.mart_events_di\", engine, index=False)\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "mart.mart_events_di", "columns": ["chemical_id", "prof_office"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table region_stats --columns investment_round,clubname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "region_stats", "columns": ["investment_round", "clubname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"flight_safety\")\nexport_to_sink(df, \"company_info\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "flight_safety", "columns": null}], "writes": [{"table": "company_info", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT furniture_id, host_country FROM attendance\", engine)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"defensespending\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "attendance", "columns": ["furniture_id", "host_country"]}], "writes": [{"table": "defensespending", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.refunds_delta SELECT athlete_id, donorgender FROM dws.dws_refunds_hourly WHERE athlete_id > 38\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": ["athlete_id", "donorgender"]}], "writes": [{"table": "ads.refunds_delta", "columns": ["athlete_id", "donorgender"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"grad_students\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "grad_students", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"courses\").toPandas()\ndf[[\"productiondate\", \"shipment_id\"]].to_sql(\"submersible_dives\", engine, index=False)\n", "labels": {"reads": [{"table": "courses", "columns": null}], "writes": [{"table": "submersible_dives", "columns": ["productiondate", "shipment_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO iron (lipstick_id, payment_method) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "iron", "columns": ["lipstick_id", "payment_method"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO plants SELECT cultural_competency_score, authors, socially_responsible FROM attribute_definitions WHERE cultural_competency_score > 384\")\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": ["cultural_competency_score", "authors", "socially_responsible"]}], "writes": [{"table": "plants", "columns": ["cultural_competency_score", "authors", "socially_responsible"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organizations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stations\")\n", "labels": {"reads": [{"table": "organizations", "columns": null}], "writes": [{"table": "stations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO bi.bi_risk_score_full SELECT machine_id, booking_end_date, vehicle_id FROM transportation_union WHERE machine_id > 58\"\n", "labels": {"reads": [{"table": "transportation_union", "columns": ["machine_id", "booking_end_date", "vehicle_id"]}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["machine_id", "booking_end_date", "vehicle_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ai_safety_papers2 --columns business_name,friend --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ai_safety_papers2", "columns": ["business_name", "friend"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.stg_exposure_daily (production_bopd, classtype) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_exposure_daily", "columns": ["production_bopd", "classtype"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO atlantic_ocean SELECT a.inspectiondate, b.trip_city FROM ref_shipping_agents a JOIN activity b ON a.donorid = b.donorid\"\n", "labels": {"reads": [{"table": "ref_shipping_agents", "columns": null}, {"table": "activity", "columns": null}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO government.city SELECT funding_source, ll_id, archeologist FROM adaptation_projects WHERE funding_source > 71\"\n", "labels": {"reads": [{"table": "adaptation_projects", "columns": ["funding_source", "ll_id", "archeologist"]}], "writes": [{"table": "government.city", "columns": ["funding_source", "ll_id", "archeologist"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incident, garment_name FROM shipment_data LIMIT 492\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO facility_production SELECT unionid, issues FROM project_timelines WHERE unionid > 213\")\n", "labels": {"reads": [{"table": "shipment_data", "columns": ["incident", "garment_name"]}, {"table": "project_timelines", "columns": ["unionid", "issues"]}], "writes": [{"table": "facility_production", "columns": ["unionid", "issues"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model pollutionincidents depends on haircaresales\ndbt build --select pollutionincidents --vars 'source: haircaresales'\n", "labels": {"reads": [{"table": "haircaresales", "columns": null}], "writes": [{"table": "pollutionincidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table infrastructure_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "infrastructure_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT pilot_name, mental_health_score FROM reverselogisticstransactions LIMIT 469\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": ["pilot_name", "mental_health_score"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT effort_id, composer FROM marine_species LIMIT 198;\nINSERT INTO ref_hotel_star_ratings SELECT share_in_percent, seasons FROM infection_rates WHERE share_in_percent > 417;\nSQL\n", "labels": {"reads": [{"table": "marine_species", "columns": ["effort_id", "composer"]}, {"table": "infection_rates", "columns": ["share_in_percent", "seasons"]}], "writes": [{"table": "ref_hotel_star_ratings", "columns": ["share_in_percent", "seasons"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO department_publications SELECT rec_engine, maintenancedate, updated_at FROM product_review WHERE rec_engine > 284\"], check=True)\n", "labels": {"reads": [{"table": "product_review", "columns": ["rec_engine", "maintenancedate", "updated_at"]}], "writes": [{"table": "department_publications", "columns": ["rec_engine", "maintenancedate", "updated_at"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO monitoring_zones SELECT a.well_depth, b.order_quantity FROM dw_risk_score_daily a JOIN fairness_scores b ON a.company = b.company\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dw_risk_score_daily", "columns": null}, {"table": "fairness_scores", "columns": null}], "writes": [{"table": "monitoring_zones", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO wastewater_treatment SELECT cause_name, police_officers FROM bus_fare_collection WHERE cause_name > 341\"\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": ["cause_name", "police_officers"]}], "writes": [{"table": "wastewater_treatment", "columns": ["cause_name", "police_officers"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT totalprice, rig_name FROM clothingsales LIMIT 472\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO rating SELECT birthdate, horizontal_bar_points, conferenceid, sale_country FROM artistsdemographics WHERE birthdate > 447\")\n", "labels": {"reads": [{"table": "clothingsales", "columns": ["totalprice", "rig_name"]}, {"table": "artistsdemographics", "columns": ["birthdate", "horizontal_bar_points", "conferenceid", "sale_country"]}], "writes": [{"table": "rating", "columns": ["birthdate", "horizontal_bar_points", "conferenceid", "sale_country"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table deep_sea_expeditions --columns wage,unique_founders --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "deep_sea_expeditions", "columns": ["wage", "unique_founders"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.date_formed > 204).all()\n# src table: spacemissions\nengine.execute(\"INSERT INTO circularsupplychain SELECT * FROM spacemissions\")\n", "labels": {"reads": [{"table": "spacemissions", "columns": null}], "writes": [{"table": "circularsupplychain", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT project_category, warehousename FROM safety_violations\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"functional_areas\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "safety_violations", "columns": ["project_category", "warehousename"]}], "writes": [{"table": "functional_areas", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO cities SELECT trip_city, vehicle_details, clinic_id FROM defenseprojects WHERE trip_city > 117\")\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["trip_city", "vehicle_details", "clinic_id"]}], "writes": [{"table": "cities", "columns": ["trip_city", "vehicle_details", "clinic_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd_sessions_hourly (scoreid, tech_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd_sessions_hourly", "columns": ["scoreid", "tech_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM yoga\", conn)\ndf.to_sql(\"public_transportation_sydney\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "yoga", "columns": null}], "writes": [{"table": "public_transportation_sydney", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT report_date, cid FROM diversity LIMIT 88\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "diversity", "columns": ["report_date", "cid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM arcticocean\"\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO refugees SELECT innovation, practice_id FROM nba WHERE innovation > 162\"\n", "labels": {"reads": [{"table": "nba", "columns": ["innovation", "practice_id"]}], "writes": [{"table": "refugees", "columns": ["innovation", "practice_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO armed_forces SELECT birth_place, transactions, store_id, assessment_score FROM bi.products_daily WHERE birth_place > 90\"\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["birth_place", "transactions", "store_id", "assessment_score"]}], "writes": [{"table": "armed_forces", "columns": ["birth_place", "transactions", "store_id", "assessment_score"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table mart.clicks_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.clicks_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO visualartprograms SELECT spacecraft_id, policyholder_id, course_name FROM manager WHERE spacecraft_id > 269\"], check=True)\n", "labels": {"reads": [{"table": "manager", "columns": ["spacecraft_id", "policyholder_id", "course_name"]}], "writes": [{"table": "visualartprograms", "columns": ["spacecraft_id", "policyholder_id", "course_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT minister, drug_id FROM warehouses LIMIT 322\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO dwd.dwd_vendors SELECT initiative, financial_wellbeing_score, transaction_amount FROM market_access WHERE initiative > 244\")\n", "labels": {"reads": [{"table": "warehouses", "columns": ["minister", "drug_id"]}, {"table": "market_access", "columns": ["initiative", "financial_wellbeing_score", "transaction_amount"]}], "writes": [{"table": "dwd.dwd_vendors", "columns": ["initiative", "financial_wellbeing_score", "transaction_amount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM broadband_customers_global\", conn)\ndf.to_sql(\"person\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}], "writes": [{"table": "person", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"episodes\")\nsrc.write.insertInto(\"passenger_trips\", overwrite=True)\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": [{"table": "passenger_trips", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dispensary_sales SELECT * FROM legacy\ncur.execute(\"SELECT judge_id, range FROM purchases LIMIT 122\")\n", "labels": {"reads": [{"table": "purchases", "columns": ["judge_id", "range"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT charging_level, claimid FROM market_access LIMIT 498;\nINSERT INTO communitycourtcases SELECT grant_start_date, problem_description, stayid FROM mental_health_parity WHERE grant_start_date > 37;\nSQL\n", "labels": {"reads": [{"table": "market_access", "columns": ["charging_level", "claimid"]}, {"table": "mental_health_parity", "columns": ["grant_start_date", "problem_description", "stayid"]}], "writes": [{"table": "communitycourtcases", "columns": ["grant_start_date", "problem_description", "stayid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO military_bases (course_completion, support_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "military_bases", "columns": ["course_completion", "support_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO community.donors SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods.ods_events_daily --columns show_id,feature_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_events_daily", "columns": ["show_id", "feature_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO performances SELECT article_id, launch_company, discount, system FROM attribute_definitions WHERE article_id > 61;\nEOF\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": ["article_id", "launch_company", "discount", "system"]}], "writes": [{"table": "performances", "columns": ["article_id", "launch_company", "discount", "system"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT order_item_status, show_id FROM dwd.sessions LIMIT 174\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO entrepreneur SELECT application_date, occupation, production_id FROM jupiter_missions WHERE application_date > 2\")\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": ["order_item_status", "show_id"]}, {"table": "jupiter_missions", "columns": ["application_date", "occupation", "production_id"]}], "writes": [{"table": "entrepreneur", "columns": ["application_date", "occupation", "production_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"tracklists\")\nsrc.write.insertInto(\"attendees\", overwrite=True)\n", "labels": {"reads": [{"table": "tracklists", "columns": null}], "writes": [{"table": "attendees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table new_schedules --columns invoice_details,team_id_br --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "new_schedules", "columns": ["invoice_details", "team_id_br"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO online_travel_agency (song_year, program_category) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "online_travel_agency", "columns": ["song_year", "program_category"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO advisor (attack_country, camera_lens_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "advisor", "columns": ["attack_country", "camera_lens_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO oceania_countries SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.coupon_use\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stay (visit_month, restaurant_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stay", "columns": ["visit_month", "restaurant_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO veteran_stats SELECT zip_postcode, safety_record, analysis_date FROM stg.stg_risk_score_hourly WHERE zip_postcode > 145\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": ["zip_postcode", "safety_record", "analysis_date"]}], "writes": [{"table": "veteran_stats", "columns": ["zip_postcode", "safety_record", "analysis_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"team\")\nsrc.write.insertInto(\"public.trips_by_day_train\", overwrite=True)\n", "labels": {"reads": [{"table": "team", "columns": null}], "writes": [{"table": "public.trips_by_day_train", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table pets --columns label_id,shares --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "pets", "columns": ["label_id", "shares"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO producersnewmexico SELECT total_attendance, requestdate, asessment_outcome_code FROM militarycyberops WHERE total_attendance > 422\"\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["total_attendance", "requestdate", "asessment_outcome_code"]}], "writes": [{"table": "producersnewmexico", "columns": ["total_attendance", "requestdate", "asessment_outcome_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT living_wage, sales_details FROM salary LIMIT 302\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "salary", "columns": ["living_wage", "sales_details"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"humanitarianmissions\")\nsrc.write.insertInto(\"instructors\", overwrite=True)\n", "labels": {"reads": [{"table": "humanitarianmissions", "columns": null}], "writes": [{"table": "instructors", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO chemicalbatches SELECT diagnosis, countid, image_data FROM attendee_demographics WHERE diagnosis > 117\"], check=True)\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": ["diagnosis", "countid", "image_data"]}], "writes": [{"table": "chemicalbatches", "columns": ["diagnosis", "countid", "image_data"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT safety_record, aid FROM baseball_teams LIMIT 267\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO site SELECT investment, local_authority FROM dp_articles WHERE investment > 118\")\n", "labels": {"reads": [{"table": "baseball_teams", "columns": ["safety_record", "aid"]}, {"table": "dp_articles", "columns": ["investment", "local_authority"]}], "writes": [{"table": "site", "columns": ["investment", "local_authority"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"workers\")\nsrc.write.insertInto(\"asia_events\", overwrite=True)\n", "labels": {"reads": [{"table": "workers", "columns": null}], "writes": [{"table": "asia_events", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table caseattorneys --columns sellingprice,hotel_chain_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "caseattorneys", "columns": ["sellingprice", "hotel_chain_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO genetics.projects SELECT left_office, animal FROM healthcare_centers WHERE left_office > 445;\nEOF\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": ["left_office", "animal"]}], "writes": [{"table": "genetics.projects", "columns": ["left_office", "animal"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fare_collection\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"drugs\")\n", "labels": {"reads": [{"table": "fare_collection", "columns": null}], "writes": [{"table": "drugs", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.coupon_use\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO workerbuildings SELECT a.account_id, b.building_id FROM financial_transactions a JOIN dwd.coupon_use_full b ON a.lastdonationdate = b.lastdonationdate\"\n", "labels": {"reads": [{"table": "financial_transactions", "columns": null}, {"table": "dwd.coupon_use_full", "columns": null}], "writes": [{"table": "workerbuildings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_orders_full SELECT is_false, subscribe_date, building_phone FROM wta_serves WHERE is_false > 373\"\n", "labels": {"reads": [{"table": "wta_serves", "columns": ["is_false", "subscribe_date", "building_phone"]}], "writes": [{"table": "ads.ads_orders_full", "columns": ["is_false", "subscribe_date", "building_phone"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO trains SELECT a.individual_id, b.part_fault_id FROM postseason a JOIN caribbean_tourists b ON a.volume = b.volume\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "postseason", "columns": null}, {"table": "caribbean_tourists", "columns": null}], "writes": [{"table": "trains", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM smart_city_projects\"\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO performance_scores SELECT catalog_publisher, num_sustainable_materials FROM student WHERE catalog_publisher > 307\"\n", "labels": {"reads": [{"table": "student", "columns": ["catalog_publisher", "num_sustainable_materials"]}], "writes": [{"table": "performance_scores", "columns": ["catalog_publisher", "num_sustainable_materials"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO environmental_impact_stats SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM middle_east_military_spending\"\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incidentid, stu_num FROM spacecraftmanufacturing LIMIT 458\")\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO contractorsales SELECT provider_id, therapy_session FROM discount_coupons WHERE provider_id > 202\")\n", "labels": {"reads": [{"table": "spacecraftmanufacturing", "columns": ["incidentid", "stu_num"]}, {"table": "discount_coupons", "columns": ["provider_id", "therapy_session"]}], "writes": [{"table": "contractorsales", "columns": ["provider_id", "therapy_session"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO ticket_sales SELECT instructor_id, passengers, plantlocation, cityid FROM carbon_sequestration WHERE instructor_id > 58\")\n", "labels": {"reads": [{"table": "carbon_sequestration", "columns": ["instructor_id", "passengers", "plantlocation", "cityid"]}], "writes": [{"table": "ticket_sales", "columns": ["instructor_id", "passengers", "plantlocation", "cityid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO hotel_chains SELECT refugee_name, devices FROM camera_lens WHERE refugee_name > 40\"\n", "labels": {"reads": [{"table": "camera_lens", "columns": ["refugee_name", "devices"]}], "writes": [{"table": "hotel_chains", "columns": ["refugee_name", "devices"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO production_yearly (hashtags, warehousename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "production_yearly", "columns": ["hashtags", "warehousename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tourism (regulation, safety_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tourism", "columns": ["regulation", "safety_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT city_area, trainingid FROM ods.sessions_daily LIMIT 94;\nINSERT INTO mart.shipments_delta SELECT is_public, medium FROM ref_document_status WHERE is_public > 487;\nSQL\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": ["city_area", "trainingid"]}, {"table": "ref_document_status", "columns": ["is_public", "medium"]}], "writes": [{"table": "mart.shipments_delta", "columns": ["is_public", "medium"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT decision, stat_type FROM season_assists LIMIT 45\")\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO affordablehousing SELECT water_consumption, custid, agegroup FROM adrprograms WHERE water_consumption > 347\")\n", "labels": {"reads": [{"table": "season_assists", "columns": ["decision", "stat_type"]}, {"table": "adrprograms", "columns": ["water_consumption", "custid", "agegroup"]}], "writes": [{"table": "affordablehousing", "columns": ["water_consumption", "custid", "agegroup"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO faculty_participates_in SELECT avg_speed, order_status, headquarters, heartrate FROM artists_valuation WHERE avg_speed > 417\"\n", "labels": {"reads": [{"table": "artists_valuation", "columns": ["avg_speed", "order_status", "headquarters", "heartrate"]}], "writes": [{"table": "faculty_participates_in", "columns": ["avg_speed", "order_status", "headquarters", "heartrate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT organizationid, char_cells FROM mart.device_log_hourly LIMIT 58\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO tasks SELECT cname, start_station_name FROM ancient_artifacts WHERE cname > 254\")\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": ["organizationid", "char_cells"]}, {"table": "ancient_artifacts", "columns": ["cname", "start_station_name"]}], "writes": [{"table": "tasks", "columns": ["cname", "start_station_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"indian_ocean_wells\").toPandas()\ndf[[\"aid_name\", \"activity_id\"]].to_sql(\"rural_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "indian_ocean_wells", "columns": null}], "writes": [{"table": "rural_projects", "columns": ["aid_name", "activity_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO worker_scores SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.data_type > 390).all()\n# src table: stellar_transactions\nengine.execute(\"INSERT INTO countries SELECT * FROM stellar_transactions\")\n", "labels": {"reads": [{"table": "stellar_transactions", "columns": null}], "writes": [{"table": "countries", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"inclusivehousing.affordablehousing\")\nsrc.write.insertInto(\"geological_survey\", overwrite=True)\n", "labels": {"reads": [{"table": "inclusivehousing.affordablehousing", "columns": null}], "writes": [{"table": "geological_survey", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO climate_mitigation_projects SELECT destinationid, trade FROM dishes WHERE destinationid > 410;\nEOF\n", "labels": {"reads": [{"table": "dishes", "columns": ["destinationid", "trade"]}], "writes": [{"table": "climate_mitigation_projects", "columns": ["destinationid", "trade"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT club_name, video_id FROM hockey_players LIMIT 175\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "hockey_players", "columns": ["club_name", "video_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO bi.bi_payments_df SELECT date_of_notes, therapy_date FROM mart.campaigns_full WHERE date_of_notes > 162\")\n", "labels": {"reads": [{"table": "mart.campaigns_full", "columns": ["date_of_notes", "therapy_date"]}], "writes": [{"table": "bi.bi_payments_df", "columns": ["date_of_notes", "therapy_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO farmer_details SELECT headquarter, org_size FROM marine_species_status WHERE headquarter > 72\"\n", "labels": {"reads": [{"table": "marine_species_status", "columns": ["headquarter", "org_size"]}], "writes": [{"table": "farmer_details", "columns": ["headquarter", "org_size"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO systems SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO experts SELECT a.deliverydate, b.collection_id FROM catalog_contents a JOIN state_usage b ON a.dockingid = b.dockingid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "catalog_contents", "columns": null}, {"table": "state_usage", "columns": null}], "writes": [{"table": "experts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO safety_incident (staff_id, measurement) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "safety_incident", "columns": ["staff_id", "measurement"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO skincare_sales SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM community_development_projects\"\n", "labels": {"reads": [{"table": "community_development_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ods.ods_users_daily SELECT sustainablepractices, pilot_id FROM bank WHERE sustainablepractices > 183\"], check=True)\n", "labels": {"reads": [{"table": "bank", "columns": ["sustainablepractices", "pilot_id"]}], "writes": [{"table": "ods.ods_users_daily", "columns": ["sustainablepractices", "pilot_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO rooms SELECT contract_start_date, advisory_id FROM marine_conservation WHERE contract_start_date > 2;\nEOF\n", "labels": {"reads": [{"table": "marine_conservation", "columns": ["contract_start_date", "advisory_id"]}], "writes": [{"table": "rooms", "columns": ["contract_start_date", "advisory_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO wastewater_facilities (card_type_code, journalist_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "wastewater_facilities", "columns": ["card_type_code", "journalist_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"museums\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"red_line\")\n", "labels": {"reads": [{"table": "museums", "columns": null}], "writes": [{"table": "red_line", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO movie_financials SELECT functional_area_description, total_spent, exhibition_id FROM dispensaries WHERE functional_area_description > 227\")\n", "labels": {"reads": [{"table": "dispensaries", "columns": ["functional_area_description", "total_spent", "exhibition_id"]}], "writes": [{"table": "movie_financials", "columns": ["functional_area_description", "total_spent", "exhibition_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.gas_production_2020 > 346).all()\n# src table: production_sites\nengine.execute(\"INSERT INTO drilling_rigs SELECT * FROM production_sites\")\n", "labels": {"reads": [{"table": "production_sites", "columns": null}], "writes": [{"table": "drilling_rigs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT floor_exercise_points, production_volume FROM student_tests_taken\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"incidents\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": ["floor_exercise_points", "production_volume"]}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO extraction_methods SELECT a.initiative_region, b.member_name FROM geological_survey a JOIN carbon_pricing b ON a.artistname = b.artistname\"\n", "labels": {"reads": [{"table": "geological_survey", "columns": null}, {"table": "carbon_pricing", "columns": null}], "writes": [{"table": "extraction_methods", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO investors SELECT a.warehouse_name, b.vrgameid FROM communication_scores a JOIN renewable_projects b ON a.trainingid = b.trainingid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "communication_scores", "columns": null}, {"table": "renewable_projects", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO online_platform (enzyme_id, pollutant_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "online_platform", "columns": ["enzyme_id", "pollutant_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO volume (host_country, product_type_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "volume", "columns": ["host_country", "product_type_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO emergency_responses SELECT hearingdate, max_wind_speed_mph FROM rating WHERE hearingdate > 321\"\n", "labels": {"reads": [{"table": "rating", "columns": ["hearingdate", "max_wind_speed_mph"]}], "writes": [{"table": "emergency_responses", "columns": ["hearingdate", "max_wind_speed_mph"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fault_log_parts\", conn)\ndf.to_sql(\"healthequitymetrics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": null}], "writes": [{"table": "healthequitymetrics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO country_landfill_capacity SELECT has_spf, vehicle_details FROM highways WHERE has_spf > 64;\nEOF\n", "labels": {"reads": [{"table": "highways", "columns": ["has_spf", "vehicle_details"]}], "writes": [{"table": "country_landfill_capacity", "columns": ["has_spf", "vehicle_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO customer_master_index SELECT * FROM legacy\ncur.execute(\"SELECT profits_in_billion, location_text FROM ref_locations LIMIT 495\")\n", "labels": {"reads": [{"table": "ref_locations", "columns": ["profits_in_billion", "location_text"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO gene SELECT * FROM legacy\ncur.execute(\"SELECT annual_revenue, market FROM nutritionfacts LIMIT 324\")\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": ["annual_revenue", "market"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT therapy_type, watch_time FROM industry_funding LIMIT 119;\nINSERT INTO state_energy SELECT budget_type_description, investmenttype FROM donationprograms WHERE budget_type_description > 428;\nSQL\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["therapy_type", "watch_time"]}, {"table": "donationprograms", "columns": ["budget_type_description", "investmenttype"]}], "writes": [{"table": "state_energy", "columns": ["budget_type_description", "investmenttype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table security_incidents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "security_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT ethical_certifications, watertemp FROM mars_rovers LIMIT 122;\nINSERT INTO ref_document_status SELECT team_id_br, centerid FROM course_attendance WHERE team_id_br > 69;\nSQL\n", "labels": {"reads": [{"table": "mars_rovers", "columns": ["ethical_certifications", "watertemp"]}, {"table": "course_attendance", "columns": ["team_id_br", "centerid"]}], "writes": [{"table": "ref_document_status", "columns": ["team_id_br", "centerid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO state_contracts SELECT fuelid, amount_waste FROM ocean_floor WHERE fuelid > 359\"], check=True)\n", "labels": {"reads": [{"table": "ocean_floor", "columns": ["fuelid", "amount_waste"]}], "writes": [{"table": "state_contracts", "columns": ["fuelid", "amount_waste"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM affiliated_with\", conn)\ndf.to_sql(\"ads.ads_payments_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": [{"table": "ads.ads_payments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table fleets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fleets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table airlines --columns clinic_id,import_country --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "airlines", "columns": ["clinic_id", "import_country"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversity\").toPandas()\ndf[[\"other_hotel_details\", \"athlete\"]].to_sql(\"student_course_registrations\", engine, index=False)\n", "labels": {"reads": [{"table": "diversity", "columns": null}], "writes": [{"table": "student_course_registrations", "columns": ["other_hotel_details", "athlete"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO company_info SELECT permit_date, creationyear, job, fertilizer_id FROM hydro_plants WHERE permit_date > 305\"\n", "labels": {"reads": [{"table": "hydro_plants", "columns": ["permit_date", "creationyear", "job", "fertilizer_id"]}], "writes": [{"table": "company_info", "columns": ["permit_date", "creationyear", "job", "fertilizer_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO activity SELECT fund_id, length_feet, advisory_id, building_type FROM average WHERE fund_id > 190\"], check=True)\n", "labels": {"reads": [{"table": "average", "columns": ["fund_id", "length_feet", "advisory_id", "building_type"]}], "writes": [{"table": "activity", "columns": ["fund_id", "length_feet", "advisory_id", "building_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversion_programs\").toPandas()\ndf[[\"lifespan\", \"num_tools\"]].to_sql(\"circular_economy_companies\", engine, index=False)\n", "labels": {"reads": [{"table": "diversion_programs", "columns": null}], "writes": [{"table": "circular_economy_companies", "columns": ["lifespan", "num_tools"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fruitimport SELECT a.employee_address_id, b.products_last_year FROM ca_menu_items a JOIN indian_ocean_fishingvessels b ON a.citation_time = b.citation_time\"\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": null}, {"table": "indian_ocean_fishingvessels", "columns": null}], "writes": [{"table": "fruitimport", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO dwd_events_delta SELECT a.book_id, b.cropid FROM reservations a JOIN players b ON a.studentid = b.studentid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "reservations", "columns": null}, {"table": "players", "columns": null}], "writes": [{"table": "dwd_events_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO wildlife_habitats SELECT handling_date, investor_details, famous_title FROM water_distribution WHERE handling_date > 89\"\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["handling_date", "investor_details", "famous_title"]}], "writes": [{"table": "wildlife_habitats", "columns": ["handling_date", "investor_details", "famous_title"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO mediators SELECT container_count, transaction_volume, starttime, sentence_id FROM satellites_in_orbit WHERE container_count > 231\"\n", "labels": {"reads": [{"table": "satellites_in_orbit", "columns": ["container_count", "transaction_volume", "starttime", "sentence_id"]}], "writes": [{"table": "mediators", "columns": ["container_count", "transaction_volume", "starttime", "sentence_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"material_production\")\nsrc.write.insertInto(\"ods.products_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "material_production", "columns": null}], "writes": [{"table": "ods.products_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO product_sales SELECT produceid, donation_id FROM sponsorship_donations WHERE produceid > 119\"\n", "labels": {"reads": [{"table": "sponsorship_donations", "columns": ["produceid", "donation_id"]}], "writes": [{"table": "product_sales", "columns": ["produceid", "donation_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO hotel_revenue (continent, lender_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "hotel_revenue", "columns": ["continent", "lender_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table incidents --columns opname,organisation_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "incidents", "columns": ["opname", "organisation_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.trainingtitle > 333).all()\n# src table: clinics\nengine.execute(\"INSERT INTO mart_campaigns_delta SELECT * FROM clinics\")\n", "labels": {"reads": [{"table": "clinics", "columns": null}], "writes": [{"table": "mart_campaigns_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO india_solar_power SELECT * FROM legacy\ncur.execute(\"SELECT gross_in_dollar, booking_id FROM open_pedagogy_exam LIMIT 119\")\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": ["gross_in_dollar", "booking_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT is_compliant, time_day FROM cloud_issues LIMIT 154\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "cloud_issues", "columns": ["is_compliant", "time_day"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO textile_sourcing SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO customer_address_history SELECT * FROM legacy\ncur.execute(\"SELECT consider_rate, mascot FROM singer LIMIT 5\")\n", "labels": {"reads": [{"table": "singer", "columns": ["consider_rate", "mascot"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artsheritage\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_payments_delta\")\n", "labels": {"reads": [{"table": "artsheritage", "columns": null}], "writes": [{"table": "ads.ads_payments_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mentalhealthparityviolations SELECT caloric_content, channel_code FROM restaurant_revenue WHERE caloric_content > 436\"\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": ["caloric_content", "channel_code"]}], "writes": [{"table": "mentalhealthparityviolations", "columns": ["caloric_content", "channel_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO gamedesign SELECT a.sales_channel, b.clean_jerk FROM arctic_research a JOIN renewable_energy_investments b ON a.retailer_id = b.retailer_id\"\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}, {"table": "renewable_energy_investments", "columns": null}], "writes": [{"table": "gamedesign", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd.dwd_risk_score_delta\"\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"funding_records\").toPandas()\ndf[[\"model_name\", \"topic\"]].to_sql(\"user_ad_interactions\", engine, index=False)\n", "labels": {"reads": [{"table": "funding_records", "columns": null}], "writes": [{"table": "user_ad_interactions", "columns": ["model_name", "topic"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT long, founding_year FROM circular_economy_companies LIMIT 274;\nINSERT INTO public_transport.passenger_count SELECT away_team_three_point, dish_name FROM open_data_initiatives WHERE away_team_three_point > 310;\nSQL\n", "labels": {"reads": [{"table": "circular_economy_companies", "columns": ["long", "founding_year"]}, {"table": "open_data_initiatives", "columns": ["away_team_three_point", "dish_name"]}], "writes": [{"table": "public_transport.passenger_count", "columns": ["away_team_three_point", "dish_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT policy_type, workshop_name FROM dws.payments_delta LIMIT 183;\nINSERT INTO public.developers SELECT customer_number, categoryid FROM product_characteristics WHERE customer_number > 300;\nSQL\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": ["policy_type", "workshop_name"]}, {"table": "product_characteristics", "columns": ["customer_number", "categoryid"]}], "writes": [{"table": "public.developers", "columns": ["customer_number", "categoryid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO deep_sea_expeditions SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.area_size > 490).all()\n# src table: player_attributes\nengine.execute(\"INSERT INTO battery_storage SELECT * FROM player_attributes\")\n", "labels": {"reads": [{"table": "player_attributes", "columns": null}], "writes": [{"table": "battery_storage", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ods.campaigns_di SELECT available_yn, genrename FROM stg.cart_item_full WHERE available_yn > 155\")\n", "labels": {"reads": [{"table": "stg.cart_item_full", "columns": ["available_yn", "genrename"]}], "writes": [{"table": "ods.campaigns_di", "columns": ["available_yn", "genrename"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT segment_id, role_name FROM chemicals_annual\", engine)\nmetrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"wholesale_orders\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": ["segment_id", "role_name"]}], "writes": [{"table": "wholesale_orders", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO marine_species_indian (session_language, trend) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "marine_species_indian", "columns": ["session_language", "trend"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"payments\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"policy_advocacy\")\n", "labels": {"reads": [{"table": "payments", "columns": null}], "writes": [{"table": "policy_advocacy", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.mart_shipments_hourly SELECT tourist_details, ticket_price, expeditionid, phone_id FROM dwd.sessions WHERE tourist_details > 199\"\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": ["tourist_details", "ticket_price", "expeditionid", "phone_id"]}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": ["tourist_details", "ticket_price", "expeditionid", "phone_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO hockey_players SELECT practiceid, category, high_temperature, race FROM mart.coupon_use_hourly WHERE practiceid > 120\"\n", "labels": {"reads": [{"table": "mart.coupon_use_hourly", "columns": ["practiceid", "category", "high_temperature", "race"]}], "writes": [{"table": "hockey_players", "columns": ["practiceid", "category", "high_temperature", "race"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.sessions SELECT effective_date, spacecraft_id, org_name, reo_type FROM product_sales WHERE effective_date > 32\"\n", "labels": {"reads": [{"table": "product_sales", "columns": ["effective_date", "spacecraft_id", "org_name", "reo_type"]}], "writes": [{"table": "ods.sessions", "columns": ["effective_date", "spacecraft_id", "org_name", "reo_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.project_name > 68).all()\n# src table: union_members\nengine.execute(\"INSERT INTO traffic SELECT * FROM union_members\")\n", "labels": {"reads": [{"table": "union_members", "columns": null}], "writes": [{"table": "traffic", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO trainmaintenance SELECT 1\"\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO areas SELECT rating_id, snatch FROM high_risk WHERE rating_id > 64\"\n", "labels": {"reads": [{"table": "high_risk", "columns": ["rating_id", "snatch"]}], "writes": [{"table": "areas", "columns": ["rating_id", "snatch"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO platform_production SELECT contract_end, royal_family_id, chemical FROM communityengagements WHERE contract_end > 168\"\n", "labels": {"reads": [{"table": "communityengagements", "columns": ["contract_end", "royal_family_id", "chemical"]}], "writes": [{"table": "platform_production", "columns": ["contract_end", "royal_family_id", "chemical"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"redundant_billing_data\")\ndump_to_target(df, \"ods.ods_users_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": null}], "writes": [{"table": "ods.ods_users_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dorm_amenity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"investment\")\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": null}], "writes": [{"table": "investment", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table danceevents --columns rooms,genderid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "danceevents", "columns": ["rooms", "genderid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO waterusage SELECT mine_location, fuelid, player_id FROM user_workouts_march WHERE mine_location > 392\"\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": ["mine_location", "fuelid", "player_id"]}], "writes": [{"table": "waterusage", "columns": ["mine_location", "fuelid", "player_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd.sessions SELECT maintenance_id, wastetype FROM aquaticfarm WHERE maintenance_id > 220\"], check=True)\n", "labels": {"reads": [{"table": "aquaticfarm", "columns": ["maintenance_id", "wastetype"]}], "writes": [{"table": "dwd.sessions", "columns": ["maintenance_id", "wastetype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthcare_budget\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_payments_delta\")\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": [{"table": "bi.bi_payments_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"multimodalhubs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"manufacturermaterials\")\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": null}], "writes": [{"table": "manufacturermaterials", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hall_of_fame\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO climatefinance SELECT a.co2_reduction_tons, b.height_feet FROM community_engagement a JOIN tv_shows b ON a.technology = b.technology\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "community_engagement", "columns": null}, {"table": "tv_shows", "columns": null}], "writes": [{"table": "climatefinance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT heritage_site_id, energytype FROM ref_budget_codes LIMIT 267\")\nimport logging\nspark.sql(\"INSERT INTO virtual_tourism SELECT unique_founders, bridgeid, launch_agency, max_wind_speed_mph FROM business_rates WHERE unique_founders > 429\")\n", "labels": {"reads": [{"table": "ref_budget_codes", "columns": ["heritage_site_id", "energytype"]}, {"table": "business_rates", "columns": ["unique_founders", "bridgeid", "launch_agency", "max_wind_speed_mph"]}], "writes": [{"table": "virtual_tourism", "columns": ["unique_founders", "bridgeid", "launch_agency", "max_wind_speed_mph"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.company > 266).all()\n# src table: satellites\nengine.execute(\"INSERT INTO functional_areas SELECT * FROM satellites\")\n", "labels": {"reads": [{"table": "satellites", "columns": null}], "writes": [{"table": "functional_areas", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO refugees SELECT skill_description, range FROM marine_conservation WHERE skill_description > 17;\nEOF\n", "labels": {"reads": [{"table": "marine_conservation", "columns": ["skill_description", "range"]}], "writes": [{"table": "refugees", "columns": ["skill_description", "range"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT attraction_type_description, artifact_name FROM worker_scores LIMIT 413;\nINSERT INTO office_locations SELECT hoursperweek, amount_outstanding, wildlife_type_id, product_category FROM ods.ods_risk_score_full WHERE hoursperweek > 215;\nSQL\n", "labels": {"reads": [{"table": "worker_scores", "columns": ["attraction_type_description", "artifact_name"]}, {"table": "ods.ods_risk_score_full", "columns": ["hoursperweek", "amount_outstanding", "wildlife_type_id", "product_category"]}], "writes": [{"table": "office_locations", "columns": ["hoursperweek", "amount_outstanding", "wildlife_type_id", "product_category"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mining_companies SELECT education_id, away_team_points, purchases FROM bike_stations WHERE education_id > 181\"], check=True)\n", "labels": {"reads": [{"table": "bike_stations", "columns": ["education_id", "away_team_points", "purchases"]}], "writes": [{"table": "mining_companies", "columns": ["education_id", "away_team_points", "purchases"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"machines\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"papers\")\n", "labels": {"reads": [{"table": "machines", "columns": null}], "writes": [{"table": "papers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bridges SELECT budget_type_code, vehicletype FROM constructionlaborstatistics WHERE budget_type_code > 45\"\n", "labels": {"reads": [{"table": "constructionlaborstatistics", "columns": ["budget_type_code", "vehicletype"]}], "writes": [{"table": "bridges", "columns": ["budget_type_code", "vehicletype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table concert_revenue --columns posts_per_day,fabrictype --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "concert_revenue", "columns": ["posts_per_day", "fabrictype"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO unionmembers SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT area_name, materialtype FROM shop\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"spacemissions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "shop", "columns": ["area_name", "materialtype"]}], "writes": [{"table": "spacemissions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table investments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "investments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table dws.dws_cart_item_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.dws_cart_item_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dysprosiumproduction SELECT city_name, co2_emission, farmid, countid FROM submarine_canyons WHERE city_name > 25;\nEOF\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": ["city_name", "co2_emission", "farmid", "countid"]}], "writes": [{"table": "dysprosiumproduction", "columns": ["city_name", "co2_emission", "farmid", "countid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cultivators\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chemicalproducts\")\n", "labels": {"reads": [{"table": "cultivators", "columns": null}], "writes": [{"table": "chemicalproducts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"cultural_competency_program\")\nsrc.write.insertInto(\"rooms\", overwrite=True)\n", "labels": {"reads": [{"table": "cultural_competency_program", "columns": null}], "writes": [{"table": "rooms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO permian_basin SELECT a.date_assigned_from, b.workoutdate FROM economic_diversification_efforts a JOIN habitat b ON a.camera_lens_id = b.camera_lens_id\"\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}, {"table": "habitat", "columns": null}], "writes": [{"table": "permian_basin", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO excavation (heritage_site_id, building_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "excavation", "columns": ["heritage_site_id", "building_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO infrastructurebudget SELECT coverage_type, contributionid FROM digital_divide_initiatives WHERE coverage_type > 444\"\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": ["coverage_type", "contributionid"]}], "writes": [{"table": "infrastructurebudget", "columns": ["coverage_type", "contributionid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM concert_sales\", conn)\ndf.to_sql(\"threats\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}], "writes": [{"table": "threats", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_tech\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_payments\")\n", "labels": {"reads": [{"table": "city_tech", "columns": null}], "writes": [{"table": "bi.bi_payments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO people_addresses (waste_amount, restorative_justice) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "people_addresses", "columns": ["waste_amount", "restorative_justice"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"project_issues\").toPandas()\ndf[[\"projecttype\", \"gender_diversity\"]].to_sql(\"platform\", engine, index=False)\n", "labels": {"reads": [{"table": "project_issues", "columns": null}], "writes": [{"table": "platform", "columns": ["projecttype", "gender_diversity"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO reviews SELECT attribute_name, name_first FROM machine WHERE attribute_name > 496\"\n", "labels": {"reads": [{"table": "machine", "columns": ["attribute_name", "name_first"]}], "writes": [{"table": "reviews", "columns": ["attribute_name", "name_first"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dwd_coupon_use_hourly SELECT incident, customer_country FROM labor_cost WHERE incident > 189;\nEOF\n", "labels": {"reads": [{"table": "labor_cost", "columns": ["incident", "customer_country"]}], "writes": [{"table": "dwd_coupon_use_hourly", "columns": ["incident", "customer_country"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nsql = \"INSERT INTO all_documents SELECT a.fault_log_entry_datetime, b.reo_type FROM stg.stg_coupon_use_di a JOIN locations_oceania b ON a.personnel = b.personnel\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_di", "columns": null}, {"table": "locations_oceania", "columns": null}], "writes": [{"table": "all_documents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"basketball_teams\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"manufacturers\")\n", "labels": {"reads": [{"table": "basketball_teams", "columns": null}], "writes": [{"table": "manufacturers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO county SELECT experience, retailer, satellite_name, complaintid FROM attorneylocationyear WHERE experience > 447\"\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": ["experience", "retailer", "satellite_name", "complaintid"]}], "writes": [{"table": "county", "columns": ["experience", "retailer", "satellite_name", "complaintid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO basketball_match SELECT productionid, ll_activity, time_of_purchase, resource_id FROM animal_population WHERE productionid > 432\"\n", "labels": {"reads": [{"table": "animal_population", "columns": ["productionid", "ll_activity", "time_of_purchase", "resource_id"]}], "writes": [{"table": "basketball_match", "columns": ["productionid", "ll_activity", "time_of_purchase", "resource_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ad_id, start_station_id FROM production LIMIT 299\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "production", "columns": ["ad_id", "start_station_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO volunteer_registration SELECT feedback_id, gradepoint FROM visitor_exhibition WHERE feedback_id > 489;\nEOF\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": ["feedback_id", "gradepoint"]}], "writes": [{"table": "volunteer_registration", "columns": ["feedback_id", "gradepoint"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO navalvessels SELECT date_assigned_to, first_year FROM construction_union WHERE date_assigned_to > 94\"\n", "labels": {"reads": [{"table": "construction_union", "columns": ["date_assigned_to", "first_year"]}], "writes": [{"table": "navalvessels", "columns": ["date_assigned_to", "first_year"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT framework_id, biomass FROM haircaresales LIMIT 240\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "haircaresales", "columns": ["framework_id", "biomass"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM athlete_wellbeing\", conn)\ndf.to_sql(\"ota_revenue\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": null}], "writes": [{"table": "ota_revenue", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"sustainability_fact\")\nsrc.write.insertInto(\"healthcare\", overwrite=True)\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": [{"table": "healthcare", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_shipments\"\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM climate_communication_projects\", conn)\ndf.to_sql(\"peakhours\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "climate_communication_projects", "columns": null}], "writes": [{"table": "peakhours", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tech_accessibility_funding SELECT practice, coalquantity, conferencename FROM heritagesites WHERE practice > 228\"], check=True)\n", "labels": {"reads": [{"table": "heritagesites", "columns": ["practice", "coalquantity", "conferencename"]}], "writes": [{"table": "tech_accessibility_funding", "columns": ["practice", "coalquantity", "conferencename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO circular_economy_initiatives SELECT * FROM legacy\ncur.execute(\"SELECT hometown, individual_first_name FROM unionnegotiations LIMIT 472\")\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": ["hometown", "individual_first_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donationsbycause SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table procedures --columns low_income_neighborhood,park_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "procedures", "columns": ["low_income_neighborhood", "park_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tracks\", conn)\ndf.to_sql(\"bi.inventory_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tracks", "columns": null}], "writes": [{"table": "bi.inventory_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainability_fact\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"takes\")\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": [{"table": "takes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model problem_log depends on shariah_compliant_loans\ndbt run --select problem_log --vars 'source: shariah_compliant_loans'\n", "labels": {"reads": [{"table": "shariah_compliant_loans", "columns": null}], "writes": [{"table": "problem_log", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.clicks_hourly SELECT * FROM legacy\ncur.execute(\"SELECT education_id, stayid FROM results LIMIT 106\")\n", "labels": {"reads": [{"table": "results", "columns": ["education_id", "stayid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO regions SELECT rebounds, quantity_containers, g_name FROM facility WHERE rebounds > 122\"\n", "labels": {"reads": [{"table": "facility", "columns": ["rebounds", "quantity_containers", "g_name"]}], "writes": [{"table": "regions", "columns": ["rebounds", "quantity_containers", "g_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO habitat SELECT * FROM legacy\ncur.execute(\"SELECT volunteerhourid, complaint_status_code FROM loan LIMIT 153\")\n", "labels": {"reads": [{"table": "loan", "columns": ["volunteerhourid", "complaint_status_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tencel_sources SELECT end_date, total_distance FROM inspection WHERE end_date > 293\"\n", "labels": {"reads": [{"table": "inspection", "columns": ["end_date", "total_distance"]}], "writes": [{"table": "tencel_sources", "columns": ["end_date", "total_distance"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO product_details SELECT * FROM legacy\ncur.execute(\"SELECT num_sustainable_materials, campaign FROM collectivebargaining LIMIT 366\")\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": ["num_sustainable_materials", "campaign"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dwd.dwd_events_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rebounds SELECT a.fiscal_year, b.start_station_id FROM support_programs a JOIN scores b ON a.seat_section = b.seat_section\"\n", "labels": {"reads": [{"table": "support_programs", "columns": null}, {"table": "scores", "columns": null}], "writes": [{"table": "rebounds", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"runs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"playerscores\")\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": [{"table": "playerscores", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mart.mart_member_point_df SELECT birth_place, country1, condition_id FROM buildings WHERE birth_place > 438;\nEOF\n", "labels": {"reads": [{"table": "buildings", "columns": ["birth_place", "country1", "condition_id"]}], "writes": [{"table": "mart.mart_member_point_df", "columns": ["birth_place", "country1", "condition_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hardware_model_name, yearadded FROM sustainableproduction\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"port\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": ["hardware_model_name", "yearadded"]}], "writes": [{"table": "port", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO waterconservationbudget (founding_location, painting_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "waterconservationbudget", "columns": ["founding_location", "painting_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO space_missions_2 SELECT * FROM legacy\ncur.execute(\"SELECT inventor_name, incident_type_code FROM freshwater_fish_farms LIMIT 185\")\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": ["inventor_name", "incident_type_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model community_policing depends on districts_india\ndbt run --models community_policing --vars '{\"src\":\"districts_india\"}'\n", "labels": {"reads": [{"table": "districts_india", "columns": null}], "writes": [{"table": "community_policing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO excavation_sites SELECT cloud_cover, initiative_name, programid, partner_id FROM debris WHERE cloud_cover > 134\"\n", "labels": {"reads": [{"table": "debris", "columns": ["cloud_cover", "initiative_name", "programid", "partner_id"]}], "writes": [{"table": "excavation_sites", "columns": ["cloud_cover", "initiative_name", "programid", "partner_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO pacific_ocean SELECT bikes_available, pricepergram FROM mentalhealthparityscores WHERE bikes_available > 103\"], check=True)\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": ["bikes_available", "pricepergram"]}], "writes": [{"table": "pacific_ocean", "columns": ["bikes_available", "pricepergram"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO iron SELECT mgr_start_date, resource, fueldate FROM areas WHERE mgr_start_date > 379\")\n", "labels": {"reads": [{"table": "areas", "columns": ["mgr_start_date", "resource", "fueldate"]}], "writes": [{"table": "iron", "columns": ["mgr_start_date", "resource", "fueldate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM review\", conn)\ndf.to_sql(\"mart_orders_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "review", "columns": null}], "writes": [{"table": "mart_orders_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"clinics\")\nsrc.write.insertInto(\"infrastructure_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "clinics", "columns": null}], "writes": [{"table": "infrastructure_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ref_shipping_agents SELECT a.number_of_platforms, b.disability_type FROM ads.ads_payments_delta a JOIN whale_sharks b ON a.date = b.date\"\n", "labels": {"reads": [{"table": "ads.ads_payments_delta", "columns": null}, {"table": "whale_sharks", "columns": null}], "writes": [{"table": "ref_shipping_agents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM shipment_data\"\n", "labels": {"reads": [{"table": "shipment_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"fieldd_info\")\nsave_to_warehouse(df, \"dwd.dwd_products\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fieldd_info", "columns": null}], "writes": [{"table": "dwd.dwd_products", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO public.forest_stats (rom_mib, amount) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "public.forest_stats", "columns": ["rom_mib", "amount"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dancefunding\")\nsrc.write.insertInto(\"aus_wellbeing\", overwrite=True)\n", "labels": {"reads": [{"table": "dancefunding", "columns": null}], "writes": [{"table": "aus_wellbeing", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO pollutionincidents (crime_date, garment_material) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "pollutionincidents", "columns": ["crime_date", "garment_material"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stateinfrastructure --columns dst_apid,amount_claimed --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stateinfrastructure", "columns": ["dst_apid", "amount_claimed"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO financial_capability_programs (faculty, co_owner_count) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "financial_capability_programs", "columns": ["faculty", "co_owner_count"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO visitordemographics SELECT a.total_spent, b.ll_activity FROM humanitarianmissions a JOIN languages b ON a.case_burden = b.case_burden\"\n", "labels": {"reads": [{"table": "humanitarianmissions", "columns": null}, {"table": "languages", "columns": null}], "writes": [{"table": "visitordemographics", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT ll_activity, testtype FROM product_categories LIMIT 124;\nINSERT INTO music_streaming SELECT product_id, biz_date, orderdate, cruelty_free FROM innovation_projects WHERE product_id > 343;\nSQL\n", "labels": {"reads": [{"table": "product_categories", "columns": ["ll_activity", "testtype"]}, {"table": "innovation_projects", "columns": ["product_id", "biz_date", "orderdate", "cruelty_free"]}], "writes": [{"table": "music_streaming", "columns": ["product_id", "biz_date", "orderdate", "cruelty_free"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO research_vessels SELECT union_members, country, negotiation_date FROM defense_contractors WHERE union_members > 184\")\n", "labels": {"reads": [{"table": "defense_contractors", "columns": ["union_members", "country", "negotiation_date"]}], "writes": [{"table": "research_vessels", "columns": ["union_members", "country", "negotiation_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO shared_ebikes SELECT location_code, initiativename FROM workersalaries WHERE location_code > 62;\nEOF\n", "labels": {"reads": [{"table": "workersalaries", "columns": ["location_code", "initiativename"]}], "writes": [{"table": "shared_ebikes", "columns": ["location_code", "initiativename"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"construction_labor\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "construction_labor", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO renewable_power SELECT claim_type, ai_id FROM economic_diversification WHERE claim_type > 334\"\n", "labels": {"reads": [{"table": "economic_diversification", "columns": ["claim_type", "ai_id"]}], "writes": [{"table": "renewable_power", "columns": ["claim_type", "ai_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO exhibition_visits SELECT emp_num, line_1_number_building FROM concerts WHERE emp_num > 270\"], check=True)\n", "labels": {"reads": [{"table": "concerts", "columns": ["emp_num", "line_1_number_building"]}], "writes": [{"table": "exhibition_visits", "columns": ["emp_num", "line_1_number_building"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vesselarrivals SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table classicgame --target-dir /tmp/land\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO virtual_visitors SELECT a.project_number, b.label_id FROM operations a JOIN yttrium_production b ON a.has_aloe_vera = b.has_aloe_vera\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "operations", "columns": null}, {"table": "yttrium_production", "columns": null}], "writes": [{"table": "virtual_visitors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"eu_data_usage\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"sustainable_urban_properties_2\")\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": null}], "writes": [{"table": "sustainable_urban_properties_2", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT journal_id, hotel_chain_name FROM employees LIMIT 321\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ads.risk_score SELECT ai_customer_service, item_type, high_temperature FROM student WHERE ai_customer_service > 65\")\n", "labels": {"reads": [{"table": "employees", "columns": ["journal_id", "hotel_chain_name"]}, {"table": "student", "columns": ["ai_customer_service", "item_type", "high_temperature"]}], "writes": [{"table": "ads.risk_score", "columns": ["ai_customer_service", "item_type", "high_temperature"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO southeast_providers SELECT life_expectancy, emp_num, club_id, outcome_description FROM automation_tech WHERE life_expectancy > 280\"\n", "labels": {"reads": [{"table": "automation_tech", "columns": ["life_expectancy", "emp_num", "club_id", "outcome_description"]}], "writes": [{"table": "southeast_providers", "columns": ["life_expectancy", "emp_num", "club_id", "outcome_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT claim_status_name, shipmenttype FROM weights LIMIT 313;\nINSERT INTO fund_investments SELECT visitid, connection FROM product_characteristics WHERE visitid > 145;\nSQL\n", "labels": {"reads": [{"table": "weights", "columns": ["claim_status_name", "shipmenttype"]}, {"table": "product_characteristics", "columns": ["visitid", "connection"]}], "writes": [{"table": "fund_investments", "columns": ["visitid", "connection"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO atlantic_ocean_fish (participated_in_open_pedagogy, views) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "atlantic_ocean_fish", "columns": ["participated_in_open_pedagogy", "views"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT innovation_id, trade FROM rural_resources LIMIT 244\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "rural_resources", "columns": ["innovation_id", "trade"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.heartrate > 153).all()\n# src table: student_course_registrations\nengine.execute(\"INSERT INTO cultural_events SELECT * FROM student_course_registrations\")\n", "labels": {"reads": [{"table": "student_course_registrations", "columns": null}], "writes": [{"table": "cultural_events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"parties\")\npush_to_output(df, \"agricultural_innovation_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "parties", "columns": null}], "writes": [{"table": "agricultural_innovation_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO recycling_centers SELECT customer, city_traffic_speed, orgid, contract_amount FROM project_issues WHERE customer > 28;\nEOF\n", "labels": {"reads": [{"table": "project_issues", "columns": ["customer", "city_traffic_speed", "orgid", "contract_amount"]}], "writes": [{"table": "recycling_centers", "columns": ["customer", "city_traffic_speed", "orgid", "contract_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO agroecology_practices (site, round_number) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "agroecology_practices", "columns": ["site", "round_number"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT product_type, donation_id FROM aquatic_species LIMIT 220\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "aquatic_species", "columns": ["product_type", "donation_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"zip_codes\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.stg_risk_score_df\")\n", "labels": {"reads": [{"table": "zip_codes", "columns": null}], "writes": [{"table": "stg.stg_risk_score_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO satellitedata SELECT a.engagementid, b.area_id FROM hospitals a JOIN artwork b ON a.contract_start = b.contract_start\"\n", "labels": {"reads": [{"table": "hospitals", "columns": null}, {"table": "artwork", "columns": null}], "writes": [{"table": "satellitedata", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT hiredate, vol_id FROM ads.ads_events_df LIMIT 21\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "ads.ads_events_df", "columns": ["hiredate", "vol_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO survey_data SELECT festival_name, trial_year, compatible_since_year, primaryaffiliation FROM therapy WHERE festival_name > 77\"\n", "labels": {"reads": [{"table": "therapy", "columns": ["festival_name", "trial_year", "compatible_since_year", "primaryaffiliation"]}], "writes": [{"table": "survey_data", "columns": ["festival_name", "trial_year", "compatible_since_year", "primaryaffiliation"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM loan\"\n", "labels": {"reads": [{"table": "loan", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table affordablehousing --target-dir /tmp/land\n", "labels": {"reads": [{"table": "affordablehousing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO contract_states SELECT complaint_id, address, people_id, employees FROM has_allergy WHERE complaint_id > 50\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["complaint_id", "address", "people_id", "employees"]}], "writes": [{"table": "contract_states", "columns": ["complaint_id", "address", "people_id", "employees"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO editor SELECT baseprice, personnelid, served_subscribers, class FROM ocean WHERE baseprice > 128\")\n", "labels": {"reads": [{"table": "ocean", "columns": ["baseprice", "personnelid", "served_subscribers", "class"]}], "writes": [{"table": "editor", "columns": ["baseprice", "personnelid", "served_subscribers", "class"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT openning_year, ota_id FROM ads.ads_exposure_daily LIMIT 351\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": ["openning_year", "ota_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO exhibition_record SELECT festival_id, num_workers, genreid FROM department_store_chain WHERE festival_id > 316;\nEOF\n", "labels": {"reads": [{"table": "department_store_chain", "columns": ["festival_id", "num_workers", "genreid"]}], "writes": [{"table": "exhibition_record", "columns": ["festival_id", "num_workers", "genreid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT port, cargo FROM whale_sharks LIMIT 71\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO bi.member_point_full SELECT programname, call_date, donorid, funding_year FROM maintenance_contracts WHERE programname > 118\")\n", "labels": {"reads": [{"table": "whale_sharks", "columns": ["port", "cargo"]}, {"table": "maintenance_contracts", "columns": ["programname", "call_date", "donorid", "funding_year"]}], "writes": [{"table": "bi.member_point_full", "columns": ["programname", "call_date", "donorid", "funding_year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT level, ticket_price FROM nutrition_facts LIMIT 162\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "nutrition_facts", "columns": ["level", "ticket_price"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ocean_acidity (num_libraries, movie) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ocean_acidity", "columns": ["num_libraries", "movie"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO checking SELECT quarter, count_id, is_operational FROM danceevents WHERE quarter > 175\"], check=True)\n", "labels": {"reads": [{"table": "danceevents", "columns": ["quarter", "count_id", "is_operational"]}], "writes": [{"table": "checking", "columns": ["quarter", "count_id", "is_operational"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT interest_group, unionname FROM esportsevents\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"undergoes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "esportsevents", "columns": ["interest_group", "unionname"]}], "writes": [{"table": "undergoes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"freightforwarding\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"researchgrants\")\n", "labels": {"reads": [{"table": "freightforwarding", "columns": null}], "writes": [{"table": "researchgrants", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"opendatainitiatives\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dw_payments\")\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": null}], "writes": [{"table": "dw_payments", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nba_games SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT conferencename, address_content FROM apac_hotel_views LIMIT 466\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["conferencename", "address_content"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO airport SELECT class, threats, founder_lgbtq FROM vaccine_administered WHERE class > 197\"\n", "labels": {"reads": [{"table": "vaccine_administered", "columns": ["class", "threats", "founder_lgbtq"]}], "writes": [{"table": "airport", "columns": ["class", "threats", "founder_lgbtq"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT financing_date, restaurantname FROM patient_outcomes\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"biosensors.projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": ["financing_date", "restaurantname"]}], "writes": [{"table": "biosensors.projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO suppliersfairlabor SELECT a.cargo, b.total_value_purchased FROM dorm_amenity a JOIN tour_guides b ON a.category = b.category\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": null}, {"table": "tour_guides", "columns": null}], "writes": [{"table": "suppliersfairlabor", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"medical_professionals\").toPandas()\ndf[[\"country_code\", \"payment_type_code\"]].to_sql(\"vessel_performance\", engine, index=False)\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}], "writes": [{"table": "vessel_performance", "columns": ["country_code", "payment_type_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT creationyear, date_of_attendance FROM dwd.dwd_orders_daily LIMIT 438\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": ["creationyear", "date_of_attendance"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model professor depends on customer_transactions\ndbt run --select professor --vars '{\"src\":\"customer_transactions\"}'\n", "labels": {"reads": [{"table": "customer_transactions", "columns": null}], "writes": [{"table": "professor", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO dws.payments_delta SELECT revenue, recruiterid, process_id, grantid FROM green_building_materials WHERE revenue > 485\"\n", "labels": {"reads": [{"table": "green_building_materials", "columns": ["revenue", "recruiterid", "process_id", "grantid"]}], "writes": [{"table": "dws.payments_delta", "columns": ["revenue", "recruiterid", "process_id", "grantid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO shark_biomass SELECT satellite_id, machine_series, operationname, room FROM instructor WHERE satellite_id > 286\"\n", "labels": {"reads": [{"table": "instructor", "columns": ["satellite_id", "machine_series", "operationname", "room"]}], "writes": [{"table": "shark_biomass", "columns": ["satellite_id", "machine_series", "operationname", "room"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO investment_strategies SELECT staff_gender, request_date FROM ai_papers WHERE staff_gender > 74;\nEOF\n", "labels": {"reads": [{"table": "ai_papers", "columns": ["staff_gender", "request_date"]}], "writes": [{"table": "investment_strategies", "columns": ["staff_gender", "request_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO concentrateprices SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO infrastructureprojects SELECT end_speed, customer_number, city_town FROM vessel_positions WHERE end_speed > 33\"\n", "labels": {"reads": [{"table": "vessel_positions", "columns": ["end_speed", "customer_number", "city_town"]}], "writes": [{"table": "infrastructureprojects", "columns": ["end_speed", "customer_number", "city_town"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"habitat\")\nupsert_to_output(df, \"safetyincidents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "safetyincidents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"public.police_calls\")\nsink_to_output(df, \"spacecraft_manufacturers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "public.police_calls", "columns": null}], "writes": [{"table": "spacecraft_manufacturers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT publication_date, moisture FROM menu_vendors LIMIT 316\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "menu_vendors", "columns": ["publication_date", "moisture"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_products_full SELECT bicycle_id, total_shipped FROM concerts WHERE bicycle_id > 44\"\n", "labels": {"reads": [{"table": "concerts", "columns": ["bicycle_id", "total_shipped"]}], "writes": [{"table": "ads.ads_products_full", "columns": ["bicycle_id", "total_shipped"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO visualartprograms (trial_year, institution_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "visualartprograms", "columns": ["trial_year", "institution_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dws.dws_member_point_df SELECT strategy, shippeddate, sale_date FROM states WHERE strategy > 167\"\n", "labels": {"reads": [{"table": "states", "columns": ["strategy", "shippeddate", "sale_date"]}], "writes": [{"table": "dws.dws_member_point_df", "columns": ["strategy", "shippeddate", "sale_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.shipments_daily (faculty_id, starting_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.shipments_daily", "columns": ["faculty_id", "starting_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO grapes SELECT a.royal_family_details, b.director FROM bridges a JOIN stg.stg_shipments_hourly b ON a.assessment_date = b.assessment_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bridges", "columns": null}, {"table": "stg.stg_shipments_hourly", "columns": null}], "writes": [{"table": "grapes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mart_refunds_delta\")\nsrc.write.insertInto(\"bi.bi_orders_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": null}], "writes": [{"table": "bi.bi_orders_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_assistance\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"packages\")\n", "labels": {"reads": [{"table": "food_assistance", "columns": null}], "writes": [{"table": "packages", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT department_id, denomination FROM infantmortalitydata\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"strategies\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": ["department_id", "denomination"]}], "writes": [{"table": "strategies", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.campaigns\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"product_characteristics\")\n", "labels": {"reads": [{"table": "dwd.campaigns", "columns": null}], "writes": [{"table": "product_characteristics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO budgets SELECT lastname, decoration_theme FROM fashion_trend_data WHERE lastname > 213\"\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": ["lastname", "decoration_theme"]}], "writes": [{"table": "budgets", "columns": ["lastname", "decoration_theme"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT aid_name, quantitysold FROM storage LIMIT 147\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "storage", "columns": ["aid_name", "quantitysold"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"aus_wellbeing\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"food_justice_contributors\")\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}], "writes": [{"table": "food_justice_contributors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO products_booked SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"dwd.dwd_risk_score_delta\")\ndump_to_store(df, \"bi_inventory_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}], "writes": [{"table": "bi_inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.inventory_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"coral_reefs\")\n", "labels": {"reads": [{"table": "dwd.inventory_df", "columns": null}], "writes": [{"table": "coral_reefs", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"ads_exposure_hourly\")\nsink_to_sink(df, \"spacecraftspeed\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": null}], "writes": [{"table": "spacecraftspeed", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dw.shipments_df\"\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"imagery_archive\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "imagery_archive", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model circular_economy_companies depends on hospital_visits\ndbt run --models circular_economy_companies --vars '{\"src\":\"hospital_visits\"}'\n", "labels": {"reads": [{"table": "hospital_visits", "columns": null}], "writes": [{"table": "circular_economy_companies", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO block SELECT decor, shares FROM donorprograms WHERE decor > 113\"\n", "labels": {"reads": [{"table": "donorprograms", "columns": ["decor", "shares"]}], "writes": [{"table": "block", "columns": ["decor", "shares"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT kills, problem_log_id FROM port_office LIMIT 303;\nINSERT INTO track SELECT actid, machinery_id FROM organisations WHERE actid > 390;\nSQL\n", "labels": {"reads": [{"table": "port_office", "columns": ["kills", "problem_log_id"]}, {"table": "organisations", "columns": ["actid", "machinery_id"]}], "writes": [{"table": "track", "columns": ["actid", "machinery_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO hotel_business_partnerships SELECT time_hour, lesson_status_code, field_id, business_name FROM threatintelligence WHERE time_hour > 454\"\n", "labels": {"reads": [{"table": "threatintelligence", "columns": ["time_hour", "lesson_status_code", "field_id", "business_name"]}], "writes": [{"table": "hotel_business_partnerships", "columns": ["time_hour", "lesson_status_code", "field_id", "business_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO equipment_maintenance SELECT * FROM legacy\ncur.execute(\"SELECT sales_in_billion, typical_selling_price FROM government.region LIMIT 161\")\n", "labels": {"reads": [{"table": "government.region", "columns": ["sales_in_billion", "typical_selling_price"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO art_exhibit_attendance SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.events SELECT sales_channel, animal FROM functional_areas WHERE sales_channel > 147\"], check=True)\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["sales_channel", "animal"]}], "writes": [{"table": "ads.events", "columns": ["sales_channel", "animal"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM field_production\", conn)\ndf.to_sql(\"rating\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "field_production", "columns": null}], "writes": [{"table": "rating", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO tracklists SELECT a.attendance_id, b.founded_year FROM grapes a JOIN mart.clicks b ON a.affiliation = b.affiliation\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "grapes", "columns": null}, {"table": "mart.clicks", "columns": null}], "writes": [{"table": "tracklists", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO ethics_violations SELECT issued_date, isfirstattendee, deliveryaddress FROM dwd.campaigns WHERE issued_date > 105\"\n", "labels": {"reads": [{"table": "dwd.campaigns", "columns": ["issued_date", "isfirstattendee", "deliveryaddress"]}], "writes": [{"table": "ethics_violations", "columns": ["issued_date", "isfirstattendee", "deliveryaddress"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"team_revenue\").toPandas()\ndf[[\"industry\", \"access_count\"]].to_sql(\"org_volunteer\", engine, index=False)\n", "labels": {"reads": [{"table": "team_revenue", "columns": null}], "writes": [{"table": "org_volunteer", "columns": ["industry", "access_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM hockey_players\"\n", "labels": {"reads": [{"table": "hockey_players", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_member_point_delta SELECT fuelid, arrival_date FROM fish_stock WHERE fuelid > 364\"\n", "labels": {"reads": [{"table": "fish_stock", "columns": ["fuelid", "arrival_date"]}], "writes": [{"table": "ods.ods_member_point_delta", "columns": ["fuelid", "arrival_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"hotel_chains\")\npersist_to_output(df, \"industrial_building_energy_efficiency\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "hotel_chains", "columns": null}], "writes": [{"table": "industrial_building_energy_efficiency", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO attorney_billing SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO co2_emission SELECT billing, browser_id FROM algorithmic_fairness_incidents_monthly WHERE billing > 228\")\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["billing", "browser_id"]}], "writes": [{"table": "co2_emission", "columns": ["billing", "browser_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO government.region SELECT total_attendance, role_name, agency, coach_name FROM excavation WHERE total_attendance > 399;\nEOF\n", "labels": {"reads": [{"table": "excavation", "columns": ["total_attendance", "role_name", "agency", "coach_name"]}], "writes": [{"table": "government.region", "columns": ["total_attendance", "role_name", "agency", "coach_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO eco_hotels (budget_type_description, speed) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "eco_hotels", "columns": ["budget_type_description", "speed"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO artifact_analysis SELECT incident_description, athlete_id, preferred_foot FROM engineer_skills WHERE incident_description > 204;\nEOF\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["incident_description", "athlete_id", "preferred_foot"]}], "writes": [{"table": "artifact_analysis", "columns": ["incident_description", "athlete_id", "preferred_foot"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT name_last, taskid FROM singer_in_concert LIMIT 260;\nINSERT INTO teaches SELECT analysis_date, events, clientid FROM player WHERE analysis_date > 475;\nSQL\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["name_last", "taskid"]}, {"table": "player", "columns": ["analysis_date", "events", "clientid"]}], "writes": [{"table": "teaches", "columns": ["analysis_date", "events", "clientid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_payments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "customer_payments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.open_year > 19).all()\n# src table: dws.dws_risk_score_daily\nengine.execute(\"INSERT INTO research_vessels SELECT * FROM dws.dws_risk_score_daily\")\n", "labels": {"reads": [{"table": "dws.dws_risk_score_daily", "columns": null}], "writes": [{"table": "research_vessels", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT virtual_tour_views, date_closed FROM ods.clicks_delta LIMIT 127\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO leo_missions SELECT royal_family_id, province, spacecraft_id FROM bioprocess_engineering WHERE royal_family_id > 395\")\n", "labels": {"reads": [{"table": "ods.clicks_delta", "columns": ["virtual_tour_views", "date_closed"]}, {"table": "bioprocess_engineering", "columns": ["royal_family_id", "province", "spacecraft_id"]}], "writes": [{"table": "leo_missions", "columns": ["royal_family_id", "province", "spacecraft_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO platformh SELECT facid, address_type_code, policyholderid, carrierid FROM winter_olympics WHERE facid > 105\"\n", "labels": {"reads": [{"table": "winter_olympics", "columns": ["facid", "address_type_code", "policyholderid", "carrierid"]}], "writes": [{"table": "platformh", "columns": ["facid", "address_type_code", "policyholderid", "carrierid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO tasks SELECT vr_platform, currency, sales_amount, size_ha FROM fairness_scores WHERE vr_platform > 319\")\n", "labels": {"reads": [{"table": "fairness_scores", "columns": ["vr_platform", "currency", "sales_amount", "size_ha"]}], "writes": [{"table": "tasks", "columns": ["vr_platform", "currency", "sales_amount", "size_ha"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewables.renewable_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"plays_games\")\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": null}], "writes": [{"table": "plays_games", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model infrastructureprojects depends on stats\ndbt run -s infrastructureprojects --vars '{\"src\":\"stats\"}'\n", "labels": {"reads": [{"table": "stats", "columns": null}], "writes": [{"table": "infrastructureprojects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO cotton_source SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_building_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.mart_events_di\")\n", "labels": {"reads": [{"table": "green_building_projects", "columns": null}], "writes": [{"table": "mart.mart_events_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO customer_contact_channels SELECT * FROM legacy\ncur.execute(\"SELECT education_id, contract_end FROM singer LIMIT 267\")\n", "labels": {"reads": [{"table": "singer", "columns": ["education_id", "contract_end"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"light_rail_lines\").toPandas()\ndf[[\"clientid\", \"advisoryid\"]].to_sql(\"plots\", engine, index=False)\n", "labels": {"reads": [{"table": "light_rail_lines", "columns": null}], "writes": [{"table": "plots", "columns": ["clientid", "advisoryid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_exposure_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart_exposure_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teams_mascots\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "teams_mascots", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO military_sales SELECT * FROM legacy\ncur.execute(\"SELECT bathroom_count, effort_id FROM vessel_incident_count LIMIT 337\")\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": ["bathroom_count", "effort_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table donors_region --columns gname,neighborhood_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "donors_region", "columns": ["gname", "neighborhood_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table labels --columns booking_status_code,last_workout_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "labels", "columns": ["booking_status_code", "last_workout_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO news_reporting SELECT cuisine_id, participant_type_code, event_type FROM city_labor_cost WHERE cuisine_id > 136\"], check=True)\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": ["cuisine_id", "participant_type_code", "event_type"]}], "writes": [{"table": "news_reporting", "columns": ["cuisine_id", "participant_type_code", "event_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO historicalcontexts SELECT a.deliveryid, b.inventor_name FROM circular_economy_companies a JOIN drug_sales b ON a.citizens = b.citizens\"\n", "labels": {"reads": [{"table": "circular_economy_companies", "columns": null}, {"table": "drug_sales", "columns": null}], "writes": [{"table": "historicalcontexts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO disaster_response SELECT a.destroyed_by_employee_id, b.jul FROM digital_trends a JOIN garments b ON a.maintenance_contract_company_id = b.maintenance_contract_company_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}, {"table": "garments", "columns": null}], "writes": [{"table": "disaster_response", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"eco_hotels\")\nsrc.write.insertInto(\"tree_habitat_associations\", overwrite=True)\n", "labels": {"reads": [{"table": "eco_hotels", "columns": null}], "writes": [{"table": "tree_habitat_associations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT servicename, school_colors FROM sustainablebrands\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"maintenance_engineers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": ["servicename", "school_colors"]}], "writes": [{"table": "maintenance_engineers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"project_timelines\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bridgerainfall\")\n", "labels": {"reads": [{"table": "project_timelines", "columns": null}], "writes": [{"table": "bridgerainfall", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO savings_programs SELECT a.person_id, b.member_id FROM factory_connections a JOIN marketing_budgets b ON a.disability = b.disability\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "factory_connections", "columns": null}, {"table": "marketing_budgets", "columns": null}], "writes": [{"table": "savings_programs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model artist_data depends on ads.ads_inventory_df\ndbt run --select artist_data --vars '{\"src\":\"ads.ads_inventory_df\"}'\n", "labels": {"reads": [{"table": "ads.ads_inventory_df", "columns": null}], "writes": [{"table": "artist_data", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table stg.orders_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT total_cost, activity FROM apartments\", engine)\nresult = value * ratio + offset\nimport logging\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"fish_feed_factories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "apartments", "columns": ["total_cost", "activity"]}], "writes": [{"table": "fish_feed_factories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"ngo_funding\")\nsink_to_target(df, \"counties\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ngo_funding", "columns": null}], "writes": [{"table": "counties", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"documents\")\nsrc.write.insertInto(\"waste_data\", overwrite=True)\n", "labels": {"reads": [{"table": "documents", "columns": null}], "writes": [{"table": "waste_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.catalog_level_name > 463).all()\n# src table: vocals\nengine.execute(\"INSERT INTO providers SELECT * FROM vocals\")\n", "labels": {"reads": [{"table": "vocals", "columns": null}], "writes": [{"table": "providers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads.vendors_delta (courtid, activity_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads.vendors_delta", "columns": ["courtid", "activity_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_cost\").toPandas()\ndf[[\"kills\", \"supplier_company_id\"]].to_sql(\"heritagesites\", engine, index=False)\n", "labels": {"reads": [{"table": "labor_cost", "columns": null}], "writes": [{"table": "heritagesites", "columns": ["kills", "supplier_company_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO therapy_session SELECT a.ingredient, b.image_name FROM exhibition_visits a JOIN pollution_control_initiatives b ON a.unit_of_measure = b.unit_of_measure\"\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": null}, {"table": "pollution_control_initiatives", "columns": null}], "writes": [{"table": "therapy_session", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"league\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "league", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO waterusage SELECT open_year, recipient FROM satellites_by_country WHERE open_year > 86\")\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": ["open_year", "recipient"]}], "writes": [{"table": "waterusage", "columns": ["open_year", "recipient"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT port_code, dockingdate FROM stg.risk_score_di\", engine)\nimport logging\ndf.to_sql(\"esa_missions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": ["port_code", "dockingdate"]}], "writes": [{"table": "esa_missions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table southeast_providers --columns artifacttype,chemical_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "southeast_providers", "columns": ["artifacttype", "chemical_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_device_log\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"habitat3\")\n", "labels": {"reads": [{"table": "mart.mart_device_log", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"patient_outcomes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"paris_real_estate\")\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": null}], "writes": [{"table": "paris_real_estate", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT launch_company, device_id FROM product_ingredient LIMIT 115\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO government.region SELECT fname, line_name, fuelconsumed FROM ads.users_full WHERE fname > 133\")\n", "labels": {"reads": [{"table": "product_ingredient", "columns": ["launch_company", "device_id"]}, {"table": "ads.users_full", "columns": ["fname", "line_name", "fuelconsumed"]}], "writes": [{"table": "government.region", "columns": ["fname", "line_name", "fuelconsumed"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"canals\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.events\")\n", "labels": {"reads": [{"table": "canals", "columns": null}], "writes": [{"table": "dws.events", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO teachers SELECT supply_volume, case_outcome FROM mart_refunds WHERE supply_volume > 467\"\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["supply_volume", "case_outcome"]}], "writes": [{"table": "teachers", "columns": ["supply_volume", "case_outcome"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT interest_group, sustainability_score FROM ocean_temperatures LIMIT 357;\nINSERT INTO budget_allocations SELECT custid, review_date FROM athletes WHERE custid > 25;\nSQL\n", "labels": {"reads": [{"table": "ocean_temperatures", "columns": ["interest_group", "sustainability_score"]}, {"table": "athletes", "columns": ["custid", "review_date"]}], "writes": [{"table": "budget_allocations", "columns": ["custid", "review_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO branch (area_size, volunteerhourid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "branch", "columns": ["area_size", "volunteerhourid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM view_unit_status\"\n", "labels": {"reads": [{"table": "view_unit_status", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ref_service_types SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO clothingsales SELECT studio_name, service_type_description FROM runs WHERE studio_name > 76\"\n", "labels": {"reads": [{"table": "runs", "columns": ["studio_name", "service_type_description"]}], "writes": [{"table": "clothingsales", "columns": ["studio_name", "service_type_description"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_source(ctx, \"stg.campaigns_df\")\npersist_to_sink(df, \"mart.mart_refunds_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": null}], "writes": [{"table": "mart.mart_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO product_catalog SELECT offset_id, treatment_date FROM shariah_compliant_products WHERE offset_id > 421\"\n", "labels": {"reads": [{"table": "shariah_compliant_products", "columns": ["offset_id", "treatment_date"]}], "writes": [{"table": "product_catalog", "columns": ["offset_id", "treatment_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"member_details\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_vendors\")\n", "labels": {"reads": [{"table": "member_details", "columns": null}], "writes": [{"table": "dwd.dwd_vendors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO building_permits SELECT a.enr, b.date_of_birth FROM dw.shipments_df a JOIN recalls b ON a.water_temp = b.water_temp\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": null}, {"table": "recalls", "columns": null}], "writes": [{"table": "building_permits", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO autonomous_testing SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT exploited, inclusivehousing FROM wastewater_plants LIMIT 91;\nINSERT INTO diversity SELECT club_name, fleet_name, mission_name, half FROM school_bus WHERE club_name > 6;\nSQL\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": ["exploited", "inclusivehousing"]}, {"table": "school_bus", "columns": ["club_name", "fleet_name", "mission_name", "half"]}], "writes": [{"table": "diversity", "columns": ["club_name", "fleet_name", "mission_name", "half"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT famous_title, school_id FROM seal_population LIMIT 130\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "seal_population", "columns": ["famous_title", "school_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"satellites_by_country\")\nsrc.write.insertInto(\"education_union\", overwrite=True)\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}], "writes": [{"table": "education_union", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO rainfall_data SELECT occupancy_rate, health_equity_metric_1, founder FROM climate_monitoring_stations WHERE occupancy_rate > 14\"\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": ["occupancy_rate", "health_equity_metric_1", "founder"]}], "writes": [{"table": "rainfall_data", "columns": ["occupancy_rate", "health_equity_metric_1", "founder"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"participants\")\nsrc.write.insertInto(\"ads.vendors_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "participants", "columns": null}], "writes": [{"table": "ads.vendors_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO space_exploration SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO stops SELECT reviewscore, hardware_colours FROM customer WHERE reviewscore > 39\")\n", "labels": {"reads": [{"table": "customer", "columns": ["reviewscore", "hardware_colours"]}], "writes": [{"table": "stops", "columns": ["reviewscore", "hardware_colours"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT borough, manufacturer_id FROM militarydrones LIMIT 169;\nINSERT INTO rural_infrastructure SELECT working_horses, total_employees, cinema_id, country_name FROM habitat3 WHERE working_horses > 224;\nSQL\n", "labels": {"reads": [{"table": "militarydrones", "columns": ["borough", "manufacturer_id"]}, {"table": "habitat3", "columns": ["working_horses", "total_employees", "cinema_id", "country_name"]}], "writes": [{"table": "rural_infrastructure", "columns": ["working_horses", "total_employees", "cinema_id", "country_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"indie_artists\").toPandas()\ndf[[\"issue\", \"platformname\"]].to_sql(\"request\", engine, index=False)\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}], "writes": [{"table": "request", "columns": ["issue", "platformname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO consumer SELECT official_native_language, profits_in_billion FROM well_production WHERE official_native_language > 57\"\n", "labels": {"reads": [{"table": "well_production", "columns": ["official_native_language", "profits_in_billion"]}], "writes": [{"table": "consumer", "columns": ["official_native_language", "profits_in_billion"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO size SELECT a.installation_year, b.promotiondate FROM humanitarianassistanceoperations a JOIN agricultural_innovation b ON a.date_account_opened = b.date_account_opened\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "humanitarianassistanceoperations", "columns": null}, {"table": "agricultural_innovation", "columns": null}], "writes": [{"table": "size", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO station_emergencies SELECT school_id, cropname FROM dws.dws_orders_full WHERE school_id > 404\"\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": ["school_id", "cropname"]}], "writes": [{"table": "station_emergencies", "columns": ["school_id", "cropname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"movie_financials\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "movie_financials", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recyclingrates\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"election\")\n", "labels": {"reads": [{"table": "recyclingrates", "columns": null}], "writes": [{"table": "election", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO labor_productivity (mental_health_status, fleet_series) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "labor_productivity", "columns": ["mental_health_status", "fleet_series"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table atlantic_ocean --columns attraction_id,bikes_available --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "atlantic_ocean", "columns": ["attraction_id", "bikes_available"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO driverstandings SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO blockchain_tech (explainability_score, evaluated_for_fairness) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "blockchain_tech", "columns": ["explainability_score", "evaluated_for_fairness"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"carbon_offset_south_america\")\nsrc.write.insertInto(\"team_members\", overwrite=True)\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": null}], "writes": [{"table": "team_members", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO assets SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_colors\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"producersnewmexico\")\n", "labels": {"reads": [{"table": "ref_colors", "columns": null}], "writes": [{"table": "producersnewmexico", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_users SELECT claim_stage_id, constructorid, agegroup, exit_strategy FROM co2_emissions WHERE claim_stage_id > 385\"\n", "labels": {"reads": [{"table": "co2_emissions", "columns": ["claim_stage_id", "constructorid", "agegroup", "exit_strategy"]}], "writes": [{"table": "stg.stg_users", "columns": ["claim_stage_id", "constructorid", "agegroup", "exit_strategy"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT violation_type, sales_id FROM coal LIMIT 304;\nINSERT INTO traffic SELECT countryid, extraction_state, studio FROM gender WHERE countryid > 415;\nSQL\n", "labels": {"reads": [{"table": "coal", "columns": ["violation_type", "sales_id"]}, {"table": "gender", "columns": ["countryid", "extraction_state", "studio"]}], "writes": [{"table": "traffic", "columns": ["countryid", "extraction_state", "studio"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO retailers SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO laptimes SELECT sector_id, producerid FROM accounts WHERE sector_id > 66\"\n", "labels": {"reads": [{"table": "accounts", "columns": ["sector_id", "producerid"]}], "writes": [{"table": "laptimes", "columns": ["sector_id", "producerid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table orgdonations --columns assets_billion,instrument --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "orgdonations", "columns": ["assets_billion", "instrument"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO supplier_ethics (sale_price, investors) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "supplier_ethics", "columns": ["sale_price", "investors"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO payments SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"enrollments\")\nsrc.write.insertInto(\"shipment\", overwrite=True)\n", "labels": {"reads": [{"table": "enrollments", "columns": null}], "writes": [{"table": "shipment", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO videos SELECT gender_group, maintenance_contract_company_id, issues FROM agri_innov WHERE gender_group > 17\"\n", "labels": {"reads": [{"table": "agri_innov", "columns": ["gender_group", "maintenance_contract_company_id", "issues"]}], "writes": [{"table": "videos", "columns": ["gender_group", "maintenance_contract_company_id", "issues"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO worker_scores SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT artistid, hispanic FROM threat_intel\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"industrial_building_energy_efficiency\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "threat_intel", "columns": ["artistid", "hispanic"]}], "writes": [{"table": "industrial_building_energy_efficiency", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT matchdate, issue_count FROM station_crime_rates LIMIT 486;\nINSERT INTO labor_hours SELECT tx_id, num_volunteers FROM bi.bi_vendors_di WHERE tx_id > 62;\nSQL\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": ["matchdate", "issue_count"]}, {"table": "bi.bi_vendors_di", "columns": ["tx_id", "num_volunteers"]}], "writes": [{"table": "labor_hours", "columns": ["tx_id", "num_volunteers"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM aus_wellbeing\"\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO passenger_trips SELECT * FROM legacy\ncur.execute(\"SELECT issue_count, publish_date FROM veteran_stats LIMIT 174\")\n", "labels": {"reads": [{"table": "veteran_stats", "columns": ["issue_count", "publish_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT advisor, detention_type_description FROM review LIMIT 41\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "review", "columns": ["advisor", "detention_type_description"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT id, facility_name FROM crops\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"community_centers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "crops", "columns": ["id", "facility_name"]}], "writes": [{"table": "community_centers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT country, mine_name FROM volunteer_events\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"conservation\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "volunteer_events", "columns": ["country", "mine_name"]}], "writes": [{"table": "conservation", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO exhibition_artworks SELECT class_section, floor_area_m2 FROM chemical_production_5 WHERE class_section > 154\"\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": ["class_section", "floor_area_m2"]}], "writes": [{"table": "exhibition_artworks", "columns": ["class_section", "floor_area_m2"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO gold SELECT president_vote, lanes FROM india_ingredient_sourcing WHERE president_vote > 414\")\n", "labels": {"reads": [{"table": "india_ingredient_sourcing", "columns": ["president_vote", "lanes"]}], "writes": [{"table": "gold", "columns": ["president_vote", "lanes"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO drug_approvals SELECT production_value, theatrename FROM company_info WHERE production_value > 291\"\n", "labels": {"reads": [{"table": "company_info", "columns": ["production_value", "theatrename"]}], "writes": [{"table": "drug_approvals", "columns": ["production_value", "theatrename"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO user_ad_interactions SELECT college, contact_staff_id, invoicedate, usage FROM urban_transportation WHERE college > 454\"], check=True)\n", "labels": {"reads": [{"table": "urban_transportation", "columns": ["college", "contact_staff_id", "invoicedate", "usage"]}], "writes": [{"table": "user_ad_interactions", "columns": ["college", "contact_staff_id", "invoicedate", "usage"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO locations_oceania SELECT problem_id, species_name, volunteerdate FROM us_military_personnel WHERE problem_id > 31\")\n", "labels": {"reads": [{"table": "us_military_personnel", "columns": ["problem_id", "species_name", "volunteerdate"]}], "writes": [{"table": "locations_oceania", "columns": ["problem_id", "species_name", "volunteerdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO members SELECT invested, sector_id FROM drug_approvals WHERE invested > 234\"], check=True)\n", "labels": {"reads": [{"table": "drug_approvals", "columns": ["invested", "sector_id"]}], "writes": [{"table": "members", "columns": ["invested", "sector_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.campaigns_di SELECT is_cruelty_free, biomass, mh_id, household_size FROM city_waste_generation WHERE is_cruelty_free > 37\"\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": ["is_cruelty_free", "biomass", "mh_id", "household_size"]}], "writes": [{"table": "mart.campaigns_di", "columns": ["is_cruelty_free", "biomass", "mh_id", "household_size"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT patient, total_cost FROM museums LIMIT 442;\nINSERT INTO ref_calendar SELECT opened_date, contractor_name, inspection_id, employeeid FROM campaigns WHERE opened_date > 314;\nSQL\n", "labels": {"reads": [{"table": "museums", "columns": ["patient", "total_cost"]}, {"table": "campaigns", "columns": ["opened_date", "contractor_name", "inspection_id", "employeeid"]}], "writes": [{"table": "ref_calendar", "columns": ["opened_date", "contractor_name", "inspection_id", "employeeid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT staff_name, length_meters FROM influencers LIMIT 102\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO third_party_companies SELECT schedule_date, stream_date, component_name FROM student_course_registrations WHERE schedule_date > 289\")\n", "labels": {"reads": [{"table": "influencers", "columns": ["staff_name", "length_meters"]}, {"table": "student_course_registrations", "columns": ["schedule_date", "stream_date", "component_name"]}], "writes": [{"table": "third_party_companies", "columns": ["schedule_date", "stream_date", "component_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM course_authors_and_tutors\", conn)\ndf.to_sql(\"product_reviews\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": null}], "writes": [{"table": "product_reviews", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM open_pedagogy_enrollment\"\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO artpieces SELECT * FROM legacy\ncur.execute(\"SELECT lesson_time, routeid FROM brandrevenue LIMIT 493\")\n", "labels": {"reads": [{"table": "brandrevenue", "columns": ["lesson_time", "routeid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"threat_severity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cuisine\")\n", "labels": {"reads": [{"table": "threat_severity", "columns": null}], "writes": [{"table": "cuisine", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO collective_bargaining SELECT enddate, cid FROM tours WHERE enddate > 375\"\n", "labels": {"reads": [{"table": "tours", "columns": ["enddate", "cid"]}], "writes": [{"table": "collective_bargaining", "columns": ["enddate", "cid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT virtual_tour_views, policy_count FROM student_course_attendance LIMIT 495;\nINSERT INTO performance_scores SELECT garment_material, contract_amount, satelliteid, spill_name FROM date WHERE garment_material > 180;\nSQL\n", "labels": {"reads": [{"table": "student_course_attendance", "columns": ["virtual_tour_views", "policy_count"]}, {"table": "date", "columns": ["garment_material", "contract_amount", "satelliteid", "spill_name"]}], "writes": [{"table": "performance_scores", "columns": ["garment_material", "contract_amount", "satelliteid", "spill_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO constructors SELECT school_type, tree_type_id, department, union_id FROM workforce_development WHERE school_type > 120\"], check=True)\n", "labels": {"reads": [{"table": "workforce_development", "columns": ["school_type", "tree_type_id", "department", "union_id"]}], "writes": [{"table": "constructors", "columns": ["school_type", "tree_type_id", "department", "union_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT status_code, ei_value FROM feed LIMIT 173\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO election SELECT pilot_id, habitat_name FROM candidates WHERE pilot_id > 353\")\n", "labels": {"reads": [{"table": "feed", "columns": ["status_code", "ei_value"]}, {"table": "candidates", "columns": ["pilot_id", "habitat_name"]}], "writes": [{"table": "election", "columns": ["pilot_id", "habitat_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT affirmative, amenid FROM community.donations\", engine)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"bus_fare_collection\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "community.donations", "columns": ["affirmative", "amenid"]}], "writes": [{"table": "bus_fare_collection", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO deep_sea_expeditions (brand_mentioned, num_stops) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "deep_sea_expeditions", "columns": ["brand_mentioned", "num_stops"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO all_programs SELECT subscribe_date, menuitemname, initiative_region, game_count FROM film WHERE subscribe_date > 223\"\n", "labels": {"reads": [{"table": "film", "columns": ["subscribe_date", "menuitemname", "initiative_region", "game_count"]}], "writes": [{"table": "all_programs", "columns": ["subscribe_date", "menuitemname", "initiative_region", "game_count"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT engagement_count, production_mwh FROM ads.ads_events_df\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"fuel_consumption\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_events_df", "columns": ["engagement_count", "production_mwh"]}], "writes": [{"table": "fuel_consumption", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO researchers (grant_name, founding_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "researchers", "columns": ["grant_name", "founding_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"excavation\").toPandas()\ndf[[\"team_id_winner\", \"age_group\"]].to_sql(\"ads.ads_exposure_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "excavation", "columns": null}], "writes": [{"table": "ads.ads_exposure_daily", "columns": ["team_id_winner", "age_group"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO stg.refunds_daily SELECT accommodation_type, incident_description, productname FROM securityincidents WHERE accommodation_type > 300\"\n", "labels": {"reads": [{"table": "securityincidents", "columns": ["accommodation_type", "incident_description", "productname"]}], "writes": [{"table": "stg.refunds_daily", "columns": ["accommodation_type", "incident_description", "productname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO gymc_members (date_closed, workforce_development) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "gymc_members", "columns": ["date_closed", "workforce_development"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_landfill_capacity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"drilling_rigs\")\n", "labels": {"reads": [{"table": "country_landfill_capacity", "columns": null}], "writes": [{"table": "drilling_rigs", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO coal (product_category_code, engineer_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "coal", "columns": ["product_category_code", "engineer_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"episodes\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO green_certification SELECT * FROM legacy\ncur.execute(\"SELECT fueldate, bandmateid FROM request LIMIT 336\")\n", "labels": {"reads": [{"table": "request", "columns": ["fueldate", "bandmateid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO fabricinventory SELECT site_name, people_id, judge_state FROM route WHERE site_name > 244\")\n", "labels": {"reads": [{"table": "route", "columns": ["site_name", "people_id", "judge_state"]}], "writes": [{"table": "fabricinventory", "columns": ["site_name", "people_id", "judge_state"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"sites_me\")\nsave_to_output(df, \"open_data_initiatives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "sites_me", "columns": null}], "writes": [{"table": "open_data_initiatives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.truck_details > 59).all()\n# src table: open_data_initiatives\nengine.execute(\"INSERT INTO eco_hotels SELECT * FROM open_data_initiatives\")\n", "labels": {"reads": [{"table": "open_data_initiatives", "columns": null}], "writes": [{"table": "eco_hotels", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trainingprograms SELECT labordate, centerid, meal_date, billing FROM film_category WHERE labordate > 20\"\n", "labels": {"reads": [{"table": "film_category", "columns": ["labordate", "centerid", "meal_date", "billing"]}], "writes": [{"table": "trainingprograms", "columns": ["labordate", "centerid", "meal_date", "billing"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO industrial_building_energy_efficiency SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM org_climate_finance\"\n", "labels": {"reads": [{"table": "org_climate_finance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT code, installed_date FROM mentalhealthscores LIMIT 201\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "mentalhealthscores", "columns": ["code", "installed_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitionsartworks\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"skincareinventory\")\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": [{"table": "skincareinventory", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO scan_dates SELECT total_beds, student_details, publish_date FROM assessment_notes WHERE total_beds > 216\"\n", "labels": {"reads": [{"table": "assessment_notes", "columns": ["total_beds", "student_details", "publish_date"]}], "writes": [{"table": "scan_dates", "columns": ["total_beds", "student_details", "publish_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO labour_productivity (market_value_billion, client) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "labour_productivity", "columns": ["market_value_billion", "client"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO defenseprojects SELECT volume, communityname, support_rate FROM stg.coupon_use_delta WHERE volume > 394\"\n", "labels": {"reads": [{"table": "stg.coupon_use_delta", "columns": ["volume", "communityname", "support_rate"]}], "writes": [{"table": "defenseprojects", "columns": ["volume", "communityname", "support_rate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO state_budget SELECT building_type, requestid, status FROM dws.dws_refunds_daily WHERE building_type > 158\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_daily", "columns": ["building_type", "requestid", "status"]}], "writes": [{"table": "state_budget", "columns": ["building_type", "requestid", "status"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO voyages SELECT subject_id, singer_id FROM africa_schema.african_mines WHERE subject_id > 425\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["subject_id", "singer_id"]}], "writes": [{"table": "voyages", "columns": ["subject_id", "singer_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_cart_item_daily\")\nsrc.write.insertInto(\"impact_investments\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_cart_item_daily", "columns": null}], "writes": [{"table": "impact_investments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table space_exploration --target-dir /tmp/land\n", "labels": {"reads": [{"table": "space_exploration", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"farm_competition\")\npersist_to_store(df, \"gamedata\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "farm_competition", "columns": null}], "writes": [{"table": "gamedata", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO section SELECT * FROM legacy\ncur.execute(\"SELECT s_id, maintenancedate FROM district_schools LIMIT 486\")\n", "labels": {"reads": [{"table": "district_schools", "columns": ["s_id", "maintenancedate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO budget_allocations SELECT professionalid, points FROM legal_technology_funding WHERE professionalid > 420;\nEOF\n", "labels": {"reads": [{"table": "legal_technology_funding", "columns": ["professionalid", "points"]}], "writes": [{"table": "budget_allocations", "columns": ["professionalid", "points"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO therapy SELECT share_in_percent, target_id, trial_name, insurancetype FROM invoices WHERE share_in_percent > 265\")\n", "labels": {"reads": [{"table": "invoices", "columns": ["share_in_percent", "target_id", "trial_name", "insurancetype"]}], "writes": [{"table": "therapy", "columns": ["share_in_percent", "target_id", "trial_name", "insurancetype"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.cart_item_full SELECT airport_id, patientid, nurse FROM inspection WHERE airport_id > 404;\nEOF\n", "labels": {"reads": [{"table": "inspection", "columns": ["airport_id", "patientid", "nurse"]}], "writes": [{"table": "stg.cart_item_full", "columns": ["airport_id", "patientid", "nurse"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mineral_extraction_us SELECT artwork_id, operation_name FROM crops_year WHERE artwork_id > 448\"], check=True)\n", "labels": {"reads": [{"table": "crops_year", "columns": ["artwork_id", "operation_name"]}], "writes": [{"table": "mineral_extraction_us", "columns": ["artwork_id", "operation_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table doctors --columns address_content,artworkyear --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "doctors", "columns": ["address_content", "artworkyear"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO rural_projects SELECT industry_4_0, trader_id, zip, mine_location FROM renewableprojects WHERE industry_4_0 > 288\"], check=True)\n", "labels": {"reads": [{"table": "renewableprojects", "columns": ["industry_4_0", "trader_id", "zip", "mine_location"]}], "writes": [{"table": "rural_projects", "columns": ["industry_4_0", "trader_id", "zip", "mine_location"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dws.dws_shipments_full SELECT species_name, prod_id, missionid, funding_amount FROM department_publications WHERE species_name > 454;\nEOF\n", "labels": {"reads": [{"table": "department_publications", "columns": ["species_name", "prod_id", "missionid", "funding_amount"]}], "writes": [{"table": "dws.dws_shipments_full", "columns": ["species_name", "prod_id", "missionid", "funding_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"artifactanalysis\")\nsrc.write.insertInto(\"unions\", overwrite=True)\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}], "writes": [{"table": "unions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO hotel_business_partnerships SELECT astronaut, reader_id, partner_id, segment_id FROM orders WHERE astronaut > 185;\nEOF\n", "labels": {"reads": [{"table": "orders", "columns": ["astronaut", "reader_id", "partner_id", "segment_id"]}], "writes": [{"table": "hotel_business_partnerships", "columns": ["astronaut", "reader_id", "partner_id", "segment_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mentalhealthproviders SELECT a.astronaut_name, b.prominence FROM climate_finance_re a JOIN fieldd_info b ON a.transit_passengers = b.transit_passengers\"\n", "labels": {"reads": [{"table": "climate_finance_re", "columns": null}, {"table": "fieldd_info", "columns": null}], "writes": [{"table": "mentalhealthproviders", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marine_species SELECT a.loan_amount, b.grant_date FROM tunnels a JOIN diversification_projects b ON a.word_count = b.word_count\"\n", "labels": {"reads": [{"table": "tunnels", "columns": null}, {"table": "diversification_projects", "columns": null}], "writes": [{"table": "marine_species", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO materials SELECT vendor_name, maintenance_date, safety_id, name_last FROM artist_info WHERE vendor_name > 412;\nEOF\n", "labels": {"reads": [{"table": "artist_info", "columns": ["vendor_name", "maintenance_date", "safety_id", "name_last"]}], "writes": [{"table": "materials", "columns": ["vendor_name", "maintenance_date", "safety_id", "name_last"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO inventory SELECT share_count, vote_percent, result, precip FROM appointment WHERE share_count > 78\"], check=True)\n", "labels": {"reads": [{"table": "appointment", "columns": ["share_count", "vote_percent", "result", "precip"]}], "writes": [{"table": "inventory", "columns": ["share_count", "vote_percent", "result", "precip"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT industry_4_0, organic_matter FROM wind_energy LIMIT 294\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "wind_energy", "columns": ["industry_4_0", "organic_matter"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"block\")\n", "labels": {"reads": [{"table": "sites", "columns": null}], "writes": [{"table": "block", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO feed SELECT date_joined_staff, yield_id, visitor_country, state FROM heritage_sites WHERE date_joined_staff > 104\"\n", "labels": {"reads": [{"table": "heritage_sites", "columns": ["date_joined_staff", "yield_id", "visitor_country", "state"]}], "writes": [{"table": "feed", "columns": ["date_joined_staff", "yield_id", "visitor_country", "state"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO healthcareaccess SELECT sale_country, porphyria, treatment_name, initiativeid FROM salinity_readings WHERE sale_country > 273\"\n", "labels": {"reads": [{"table": "salinity_readings", "columns": ["sale_country", "porphyria", "treatment_name", "initiativeid"]}], "writes": [{"table": "healthcareaccess", "columns": ["sale_country", "porphyria", "treatment_name", "initiativeid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organic_cosmetics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"manager\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "manager", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"product_ingredient\")\nupsert_to_warehouse(df, \"voting_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "product_ingredient", "columns": null}], "writes": [{"table": "voting_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO rural_hospitals SELECT * FROM legacy\ncur.execute(\"SELECT black, handling_date FROM video_content LIMIT 486\")\n", "labels": {"reads": [{"table": "video_content", "columns": ["black", "handling_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"imagery_archive\").toPandas()\ndf[[\"menu_type\", \"fuelconsumed\"]].to_sql(\"trade_history\", engine, index=False)\n", "labels": {"reads": [{"table": "imagery_archive", "columns": null}], "writes": [{"table": "trade_history", "columns": ["menu_type", "fuelconsumed"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.spacecraft_model > 498).all()\n# src table: fabrics\nengine.execute(\"INSERT INTO bi.bi_orders_delta SELECT * FROM fabrics\")\n", "labels": {"reads": [{"table": "fabrics", "columns": null}], "writes": [{"table": "bi.bi_orders_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT call_time, all_games FROM spacecraft\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"behavior_incident\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["call_time", "all_games"]}], "writes": [{"table": "behavior_incident", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO sports_events SELECT calendar_date, followers, donor_name FROM mart_payments_df WHERE calendar_date > 437\")\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": ["calendar_date", "followers", "donor_name"]}], "writes": [{"table": "sports_events", "columns": ["calendar_date", "followers", "donor_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO brandrevenue SELECT strain_name, loadingstart FROM ods.ods_coupon_use_delta WHERE strain_name > 83\"\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_delta", "columns": ["strain_name", "loadingstart"]}], "writes": [{"table": "brandrevenue", "columns": ["strain_name", "loadingstart"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ancient_cultures --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ancient_cultures", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads_exposure_hourly SELECT booking_date, publication_year, employee_id, completion_status FROM producersnewmexico WHERE booking_date > 80\"\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": ["booking_date", "publication_year", "employee_id", "completion_status"]}], "writes": [{"table": "ads_exposure_hourly", "columns": ["booking_date", "publication_year", "employee_id", "completion_status"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM research_staff\"\n", "labels": {"reads": [{"table": "research_staff", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO bi.bi_inventory_delta SELECT product_price, awardid FROM maintenancerequests WHERE product_price > 112\")\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": ["product_price", "awardid"]}], "writes": [{"table": "bi.bi_inventory_delta", "columns": ["product_price", "awardid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_incident_type\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mars_rovers\")\n", "labels": {"reads": [{"table": "ref_incident_type", "columns": null}], "writes": [{"table": "mars_rovers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"coal_reserves\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"budget\")\n", "labels": {"reads": [{"table": "coal_reserves", "columns": null}], "writes": [{"table": "budget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_finance_organizations\"\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO circular_economy_initiatives SELECT fish_id, round_type FROM asteroids WHERE fish_id > 362\")\n", "labels": {"reads": [{"table": "asteroids", "columns": ["fish_id", "round_type"]}], "writes": [{"table": "circular_economy_initiatives", "columns": ["fish_id", "round_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO excavation_sites SELECT fund_name, dec, price_per_gram FROM drama_workshop_groups WHERE fund_name > 58\"\n", "labels": {"reads": [{"table": "drama_workshop_groups", "columns": ["fund_name", "dec", "price_per_gram"]}], "writes": [{"table": "excavation_sites", "columns": ["fund_name", "dec", "price_per_gram"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cargos SELECT petid, median_home_value FROM educationprograms WHERE petid > 339\"\n", "labels": {"reads": [{"table": "educationprograms", "columns": ["petid", "median_home_value"]}], "writes": [{"table": "cargos", "columns": ["petid", "median_home_value"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO labor_practices SELECT exoplanet, paritystatus, rig_name, port_name FROM stateinfrastructure WHERE exoplanet > 126\"\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": ["exoplanet", "paritystatus", "rig_name", "port_name"]}], "writes": [{"table": "labor_practices", "columns": ["exoplanet", "paritystatus", "rig_name", "port_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO vessel_registry SELECT quantity, class, project FROM exhibition_artworks WHERE quantity > 197\")\n", "labels": {"reads": [{"table": "exhibition_artworks", "columns": ["quantity", "class", "project"]}], "writes": [{"table": "vessel_registry", "columns": ["quantity", "class", "project"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gymnast\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"checking\")\n", "labels": {"reads": [{"table": "gymnast", "columns": null}], "writes": [{"table": "checking", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.reader_id > 474).all()\n# src table: route_fares\nengine.execute(\"INSERT INTO mineral_extraction SELECT * FROM route_fares\")\n", "labels": {"reads": [{"table": "route_fares", "columns": null}], "writes": [{"table": "mineral_extraction", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"field4_precip\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"humanitarianassistanceoperations\")\n", "labels": {"reads": [{"table": "field4_precip", "columns": null}], "writes": [{"table": "humanitarianassistanceoperations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO educationprograms SELECT a.job, b.region FROM guests a JOIN co2_emission b ON a.number_thousands = b.number_thousands\"\n", "labels": {"reads": [{"table": "guests", "columns": null}, {"table": "co2_emission", "columns": null}], "writes": [{"table": "educationprograms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"operations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"waste_management_projects\")\n", "labels": {"reads": [{"table": "operations", "columns": null}], "writes": [{"table": "waste_management_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO space_debris SELECT award, community, typeid FROM dorm WHERE award > 448\"], check=True)\n", "labels": {"reads": [{"table": "dorm", "columns": ["award", "community", "typeid"]}], "writes": [{"table": "space_debris", "columns": ["award", "community", "typeid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO animal_budget SELECT a.individual_id, b.recipient_id FROM communityengagements a JOIN smartcitycosts b ON a.engagement = b.engagement\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "communityengagements", "columns": null}, {"table": "smartcitycosts", "columns": null}], "writes": [{"table": "animal_budget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO water_conservation_brazil SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO course_authors_and_tutors SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"pediatricians\")\nsrc.write.insertInto(\"mining.company\", overwrite=True)\n", "labels": {"reads": [{"table": "pediatricians", "columns": null}], "writes": [{"table": "mining.company", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"features\").toPandas()\ndf[[\"complete_date\", \"worker_id\"]].to_sql(\"traffic\", engine, index=False)\n", "labels": {"reads": [{"table": "features", "columns": null}], "writes": [{"table": "traffic", "columns": ["complete_date", "worker_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO job_postings SELECT claim_type, enable_third_party_ads FROM well_production WHERE claim_type > 409\"\n", "labels": {"reads": [{"table": "well_production", "columns": ["claim_type", "enable_third_party_ads"]}], "writes": [{"table": "job_postings", "columns": ["claim_type", "enable_third_party_ads"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT phone, socialimpactscore FROM festivals LIMIT 203\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "festivals", "columns": ["phone", "socialimpactscore"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"biosensors.projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT tourist_details, chemical_type FROM feedback LIMIT 28\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO crop_yield SELECT albumname, aid_name, years_working, participation_id FROM conservation WHERE albumname > 172\")\n", "labels": {"reads": [{"table": "feedback", "columns": ["tourist_details", "chemical_type"]}, {"table": "conservation", "columns": ["albumname", "aid_name", "years_working", "participation_id"]}], "writes": [{"table": "crop_yield", "columns": ["albumname", "aid_name", "years_working", "participation_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT stu_dob, district_name FROM species_observations LIMIT 214\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO battery_projects SELECT num_courses, ai_adoption, staff_gender, dockingid FROM casesbyyear WHERE num_courses > 114\")\n", "labels": {"reads": [{"table": "species_observations", "columns": ["stu_dob", "district_name"]}, {"table": "casesbyyear", "columns": ["num_courses", "ai_adoption", "staff_gender", "dockingid"]}], "writes": [{"table": "battery_projects", "columns": ["num_courses", "ai_adoption", "staff_gender", "dockingid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO colorado_river_basin SELECT * FROM legacy\ncur.execute(\"SELECT contract_value, entrydate FROM bias_categories LIMIT 453\")\n", "labels": {"reads": [{"table": "bias_categories", "columns": ["contract_value", "entrydate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO community_development_projects SELECT credits, status_of_thing_code, access_count, billing_state FROM ads.refunds WHERE credits > 367\")\n", "labels": {"reads": [{"table": "ads.refunds", "columns": ["credits", "status_of_thing_code", "access_count", "billing_state"]}], "writes": [{"table": "community_development_projects", "columns": ["credits", "status_of_thing_code", "access_count", "billing_state"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"trainings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"monthly_temp\")\n", "labels": {"reads": [{"table": "trainings", "columns": null}], "writes": [{"table": "monthly_temp", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table carbon_prices --target-dir /tmp/land\n", "labels": {"reads": [{"table": "carbon_prices", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"assignedto\")\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": [{"table": "assignedto", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO portfolios (mountain_id, served_subscribers) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "portfolios", "columns": ["mountain_id", "served_subscribers"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO research_grants SELECT birth_date, player_id, campusfee FROM timber_production WHERE birth_date > 25\"\n", "labels": {"reads": [{"table": "timber_production", "columns": ["birth_date", "player_id", "campusfee"]}], "writes": [{"table": "research_grants", "columns": ["birth_date", "player_id", "campusfee"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO school SELECT pricepergram, threat, vaccinations, grant_amount FROM equipment_sales WHERE pricepergram > 400\"\n", "labels": {"reads": [{"table": "equipment_sales", "columns": ["pricepergram", "threat", "vaccinations", "grant_amount"]}], "writes": [{"table": "school", "columns": ["pricepergram", "threat", "vaccinations", "grant_amount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.events (farm_id, rental_rate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.events", "columns": ["farm_id", "rental_rate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_sessions\").toPandas()\ndf[[\"team_id\", \"supplychainid\"]].to_sql(\"virtual_tours\", engine, index=False)\n", "labels": {"reads": [{"table": "ods_sessions", "columns": null}], "writes": [{"table": "virtual_tours", "columns": ["team_id", "supplychainid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"market_trends\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "market_trends", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT practice, headquarters FROM customer_address_history LIMIT 104\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO mart.campaigns_full SELECT staff_last_name, is_autonomous FROM tourism_activities WHERE staff_last_name > 83\")\n", "labels": {"reads": [{"table": "customer_address_history", "columns": ["practice", "headquarters"]}, {"table": "tourism_activities", "columns": ["staff_last_name", "is_autonomous"]}], "writes": [{"table": "mart.campaigns_full", "columns": ["staff_last_name", "is_autonomous"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ucl_top10 SELECT all_home, state, visit_year FROM onlineengagement WHERE all_home > 47\"\n", "labels": {"reads": [{"table": "onlineengagement", "columns": ["all_home", "state", "visit_year"]}], "writes": [{"table": "ucl_top10", "columns": ["all_home", "state", "visit_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO courses SELECT living_wage, centerid, num_stops, mealname FROM fish_stock WHERE living_wage > 372\"], check=True)\n", "labels": {"reads": [{"table": "fish_stock", "columns": ["living_wage", "centerid", "num_stops", "mealname"]}], "writes": [{"table": "courses", "columns": ["living_wage", "centerid", "num_stops", "mealname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"shipments\")\nwrite_to_target(df, \"equipment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "shipments", "columns": null}], "writes": [{"table": "equipment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart_cart_item_di (recipe_id, floor_exercise_points) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart_cart_item_di", "columns": ["recipe_id", "floor_exercise_points"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO dwd.dwd_campaigns SELECT market_value_in_billion, round_amount, recordid, menu_item_id FROM ca_menu_items WHERE market_value_in_billion > 404\")\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": ["market_value_in_billion", "round_amount", "recordid", "menu_item_id"]}], "writes": [{"table": "dwd.dwd_campaigns", "columns": ["market_value_in_billion", "round_amount", "recordid", "menu_item_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO plots SELECT activity_type, client_first_name FROM campaigns_2023 WHERE activity_type > 190;\nEOF\n", "labels": {"reads": [{"table": "campaigns_2023", "columns": ["activity_type", "client_first_name"]}], "writes": [{"table": "plots", "columns": ["activity_type", "client_first_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT booked_amount, oct FROM material\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"tree_species\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "material", "columns": ["booked_amount", "oct"]}], "writes": [{"table": "tree_species", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO campuses SELECT socially_responsible, program_type, aircraft FROM dw_payments WHERE socially_responsible > 246\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": ["socially_responsible", "program_type", "aircraft"]}], "writes": [{"table": "campuses", "columns": ["socially_responsible", "program_type", "aircraft"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"farm_competition\")\nupsert_to_output(df, \"esportsteamsafrica\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "farm_competition", "columns": null}], "writes": [{"table": "esportsteamsafrica", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO socially_responsible_loans SELECT a.main_industry, b.supplychainid FROM branch a JOIN mart.mart_refunds_di b ON a.promotiondate = b.promotiondate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "branch", "columns": null}, {"table": "mart.mart_refunds_di", "columns": null}], "writes": [{"table": "socially_responsible_loans", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO ods.sessions SELECT initiative, committee, president_vote FROM ocean_depths WHERE initiative > 224\"\n", "labels": {"reads": [{"table": "ocean_depths", "columns": ["initiative", "committee", "president_vote"]}], "writes": [{"table": "ods.sessions", "columns": ["initiative", "committee", "president_vote"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"habitat3\")\nsrc.write.insertInto(\"waste_production\", overwrite=True)\n", "labels": {"reads": [{"table": "habitat3", "columns": null}], "writes": [{"table": "waste_production", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO suburbs SELECT a.role, b.industry FROM ads.refunds_delta a JOIN assignedto b ON a.building_id = b.building_id\"\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": null}, {"table": "assignedto", "columns": null}], "writes": [{"table": "suburbs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO device SELECT a.provider_parity_score, b.primary_conference FROM mentalhealthparityviolations a JOIN restorative_justice_programs b ON a.element = b.element\"\n", "labels": {"reads": [{"table": "mentalhealthparityviolations", "columns": null}, {"table": "restorative_justice_programs", "columns": null}], "writes": [{"table": "device", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO participation (reo_type, loadingstart) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "participation", "columns": ["reo_type", "loadingstart"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model faculty depends on contract_timeline\ndbt build --select faculty --vars '{\"src\":\"contract_timeline\"}'\n", "labels": {"reads": [{"table": "contract_timeline", "columns": null}], "writes": [{"table": "faculty", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM university\"\n", "labels": {"reads": [{"table": "university", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT change_date, person_name FROM strategies\", engine)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"wholesale_orders\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "strategies", "columns": ["change_date", "person_name"]}], "writes": [{"table": "wholesale_orders", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.parameters > 137).all()\n# src table: fan_purchases\nengine.execute(\"INSERT INTO shared_rides_tokyo SELECT * FROM fan_purchases\")\n", "labels": {"reads": [{"table": "fan_purchases", "columns": null}], "writes": [{"table": "shared_rides_tokyo", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stars\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"rural_feeder_roads\")\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": [{"table": "rural_feeder_roads", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO solar_farms SELECT player, machine, num_solo_exhibitions, num_shariah_compliant_investments FROM category_revenue WHERE player > 264\"\n", "labels": {"reads": [{"table": "category_revenue", "columns": ["player", "machine", "num_solo_exhibitions", "num_shariah_compliant_investments"]}], "writes": [{"table": "solar_farms", "columns": ["player", "machine", "num_solo_exhibitions", "num_shariah_compliant_investments"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table grant --target-dir /tmp/land\n", "labels": {"reads": [{"table": "grant", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO restorative_justice_sentences SELECT unionid, shippeddate FROM militaryoperations WHERE unionid > 139\"\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["unionid", "shippeddate"]}], "writes": [{"table": "restorative_justice_sentences", "columns": ["unionid", "shippeddate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"trees\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "trees", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO chemical_production_5 SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO environmental_impact_stats SELECT a.line_id, b.event_name FROM ocean_pollution a JOIN org_climate_finance b ON a.kills = b.kills\"\n", "labels": {"reads": [{"table": "ocean_pollution", "columns": null}, {"table": "org_climate_finance", "columns": null}], "writes": [{"table": "environmental_impact_stats", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"city_properties\")\nsrc.write.insertInto(\"list\", overwrite=True)\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": [{"table": "list", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"legalaidrequests\")\nwrite_to_target(df, \"student_course_registrations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": null}], "writes": [{"table": "student_course_registrations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table ods_products_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"patents\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"economic_diversification\")\n", "labels": {"reads": [{"table": "patents", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table farms --columns tree_species,number_of_platforms --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "farms", "columns": ["tree_species", "number_of_platforms"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.site_name > 423).all()\n# src table: ai_systems\nengine.execute(\"INSERT INTO staff_members SELECT * FROM ai_systems\")\n", "labels": {"reads": [{"table": "ai_systems", "columns": null}], "writes": [{"table": "staff_members", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO rainfall_data (strain_type, has_parabens) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "rainfall_data", "columns": ["strain_type", "has_parabens"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.payments_daily SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO cargo_handling SELECT driver_id, fine_amount FROM counties WHERE driver_id > 276\")\n", "labels": {"reads": [{"table": "counties", "columns": ["driver_id", "fine_amount"]}], "writes": [{"table": "cargo_handling", "columns": ["driver_id", "fine_amount"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO culturalpractices SELECT a.investmentid, b.crispr_id FROM concert_sales a JOIN material b ON a.accelerator_id = b.accelerator_id\"\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}, {"table": "material", "columns": null}], "writes": [{"table": "culturalpractices", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ref_product_categories SELECT a.neighborhoodid, b.case_id FROM inspection a JOIN appellations b ON a.festival_id = b.festival_id\"\n", "labels": {"reads": [{"table": "inspection", "columns": null}, {"table": "appellations", "columns": null}], "writes": [{"table": "ref_product_categories", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO location SELECT treatment_id, prod_id FROM factories_africa WHERE treatment_id > 22\"\n", "labels": {"reads": [{"table": "factories_africa", "columns": ["treatment_id", "prod_id"]}], "writes": [{"table": "location", "columns": ["treatment_id", "prod_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sustainability_certified, expeditionid FROM bi.bi_sessions_daily LIMIT 94\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO eventattendance SELECT start_time, grant_amount, characteristic_data_type FROM bi.bi_campaigns_delta WHERE start_time > 175\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": ["sustainability_certified", "expeditionid"]}, {"table": "bi.bi_campaigns_delta", "columns": ["start_time", "grant_amount", "characteristic_data_type"]}], "writes": [{"table": "eventattendance", "columns": ["start_time", "grant_amount", "characteristic_data_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO festivals SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.ads_member_point_daily SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"infection_rates\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"files\")\n", "labels": {"reads": [{"table": "infection_rates", "columns": null}], "writes": [{"table": "files", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model trains depends on class\ndbt run -s trains --vars '{\"src\":\"class\"}'\n", "labels": {"reads": [{"table": "class", "columns": null}], "writes": [{"table": "trains", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO africa_schema.african_mines SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel_incident_count SELECT founder_gender, news_outlet FROM customer_master_index WHERE founder_gender > 9\"\n", "labels": {"reads": [{"table": "customer_master_index", "columns": ["founder_gender", "news_outlet"]}], "writes": [{"table": "vessel_incident_count", "columns": ["founder_gender", "news_outlet"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"stg.stg_coupon_use_di\")\nupsert_to_target(df, \"ads.ads_exposure_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_di", "columns": null}], "writes": [{"table": "ads.ads_exposure_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climber\").toPandas()\ndf[[\"sqft\", \"heartrate\"]].to_sql(\"wind_farms\", engine, index=False)\n", "labels": {"reads": [{"table": "climber", "columns": null}], "writes": [{"table": "wind_farms", "columns": ["sqft", "heartrate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthcareaccess\").toPandas()\ndf[[\"testtype\", \"company_name\"]].to_sql(\"artists_valuation\", engine, index=False)\n", "labels": {"reads": [{"table": "healthcareaccess", "columns": null}], "writes": [{"table": "artists_valuation", "columns": ["testtype", "company_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO government.city (grant_end_date, ai_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "government.city", "columns": ["grant_end_date", "ai_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO cerium_production SELECT transactionid, minename, is_hybrid, plant_id FROM recycling_centers WHERE transactionid > 94\"\n", "labels": {"reads": [{"table": "recycling_centers", "columns": ["transactionid", "minename", "is_hybrid", "plant_id"]}], "writes": [{"table": "cerium_production", "columns": ["transactionid", "minename", "is_hybrid", "plant_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO book SELECT grantid, founded, last_name FROM labor_statistics WHERE grantid > 190\"\n", "labels": {"reads": [{"table": "labor_statistics", "columns": ["grantid", "founded", "last_name"]}], "writes": [{"table": "book", "columns": ["grantid", "founded", "last_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.coupon_use_daily (round_type, offender_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.coupon_use_daily", "columns": ["round_type", "offender_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO explainableai SELECT financing_date, game_genre FROM paper_data WHERE financing_date > 386\"\n", "labels": {"reads": [{"table": "paper_data", "columns": ["financing_date", "game_genre"]}], "writes": [{"table": "explainableai", "columns": ["financing_date", "game_genre"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO average SELECT catalog_level_name, partner, service_type_description FROM recycled_polyester WHERE catalog_level_name > 217\"\n", "labels": {"reads": [{"table": "recycled_polyester", "columns": ["catalog_level_name", "partner", "service_type_description"]}], "writes": [{"table": "average", "columns": ["catalog_level_name", "partner", "service_type_description"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM smartcitytech\"\n", "labels": {"reads": [{"table": "smartcitytech", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO volunteer_hours SELECT evaluated_for_fairness, production_value, productionrate FROM stock WHERE evaluated_for_fairness > 367\"], check=True)\n", "labels": {"reads": [{"table": "stock", "columns": ["evaluated_for_fairness", "production_value", "productionrate"]}], "writes": [{"table": "volunteer_hours", "columns": ["evaluated_for_fairness", "production_value", "productionrate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"basketball_teams\")\nsrc.write.insertInto(\"urban_farms\", overwrite=True)\n", "labels": {"reads": [{"table": "basketball_teams", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table vessel_registry --target-dir /tmp/land\n", "labels": {"reads": [{"table": "vessel_registry", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT postal_code, news_story_id FROM flight_emissions LIMIT 190\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "flight_emissions", "columns": ["postal_code", "news_story_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM spaceradar\", conn)\ndf.to_sql(\"production_rare_earth_elements\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "spaceradar", "columns": null}], "writes": [{"table": "production_rare_earth_elements", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table midwest_region --target-dir /tmp/land\n", "labels": {"reads": [{"table": "midwest_region", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.program_date > 377).all()\n# src table: has_allergy\nengine.execute(\"INSERT INTO ratings SELECT * FROM has_allergy\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": null}], "writes": [{"table": "ratings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO episodes SELECT digital, treatment_date FROM player_f WHERE digital > 458\")\n", "labels": {"reads": [{"table": "player_f", "columns": ["digital", "treatment_date"]}], "writes": [{"table": "episodes", "columns": ["digital", "treatment_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"factory_workers\")\nsrc.write.insertInto(\"awards\", overwrite=True)\n", "labels": {"reads": [{"table": "factory_workers", "columns": null}], "writes": [{"table": "awards", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO digital_trends SELECT event_type, debate_id FROM stg.stg_users_di WHERE event_type > 119\"\n", "labels": {"reads": [{"table": "stg.stg_users_di", "columns": ["event_type", "debate_id"]}], "writes": [{"table": "digital_trends", "columns": ["event_type", "debate_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO item (applicant, committee) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "item", "columns": ["applicant", "committee"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO hotel_tech_adoptions SELECT year_join, next_entry_id FROM dws_coupon_use WHERE year_join > 71\"\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["year_join", "next_entry_id"]}], "writes": [{"table": "hotel_tech_adoptions", "columns": ["year_join", "next_entry_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"disasters\")\nsink_to_warehouse(df, \"list\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "disasters", "columns": null}], "writes": [{"table": "list", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO league SELECT * FROM legacy\ncur.execute(\"SELECT factory, dish_type FROM industry_funding LIMIT 121\")\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["factory", "dish_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO africa_schema.african_mines SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sustainable, onscholarship FROM stg.events_hourly LIMIT 193;\nINSERT INTO projects SELECT session_id, order_details, products_this_year FROM user WHERE session_id > 196;\nSQL\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": ["sustainable", "onscholarship"]}, {"table": "user", "columns": ["session_id", "order_details", "products_this_year"]}], "writes": [{"table": "projects", "columns": ["session_id", "order_details", "products_this_year"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO intelligence_agents SELECT * FROM legacy\ncur.execute(\"SELECT safety_record, individual_last_name FROM bi.bi_events_df LIMIT 84\")\n", "labels": {"reads": [{"table": "bi.bi_events_df", "columns": ["safety_record", "individual_last_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT worker, hosts FROM shariah_financing\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"countries\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "shariah_financing", "columns": ["worker", "hosts"]}], "writes": [{"table": "countries", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO healthcare SELECT studentid, instructor_id, range FROM useracct WHERE studentid > 111\"\n", "labels": {"reads": [{"table": "useracct", "columns": ["studentid", "instructor_id", "range"]}], "writes": [{"table": "healthcare", "columns": ["studentid", "instructor_id", "range"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT project, src_apid FROM tracklists LIMIT 64;\nINSERT INTO pilot_record SELECT closuredate, destroyed_by_employee_id, defendant_id, accommodationtype FROM eu_data_usage WHERE closuredate > 80;\nSQL\n", "labels": {"reads": [{"table": "tracklists", "columns": ["project", "src_apid"]}, {"table": "eu_data_usage", "columns": ["closuredate", "destroyed_by_employee_id", "defendant_id", "accommodationtype"]}], "writes": [{"table": "pilot_record", "columns": ["closuredate", "destroyed_by_employee_id", "defendant_id", "accommodationtype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ca_menu_items SELECT labor_practice, traveler_id, workers FROM bi.bi_inventory_delta WHERE labor_practice > 166\"\n", "labels": {"reads": [{"table": "bi.bi_inventory_delta", "columns": ["labor_practice", "traveler_id", "workers"]}], "writes": [{"table": "ca_menu_items", "columns": ["labor_practice", "traveler_id", "workers"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO open_pedagogy_courses SELECT * FROM legacy\ncur.execute(\"SELECT planned_delivery_date, salinity FROM canada_tech LIMIT 478\")\n", "labels": {"reads": [{"table": "canada_tech", "columns": ["planned_delivery_date", "salinity"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO customersregion SELECT donorgender, restaurant_id, farm_name FROM animal_populations WHERE donorgender > 116\")\n", "labels": {"reads": [{"table": "animal_populations", "columns": ["donorgender", "restaurant_id", "farm_name"]}], "writes": [{"table": "customersregion", "columns": ["donorgender", "restaurant_id", "farm_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.risk_score_df SELECT facility_code, posts_per_day, height FROM gardens WHERE facility_code > 106\"\n", "labels": {"reads": [{"table": "gardens", "columns": ["facility_code", "posts_per_day", "height"]}], "writes": [{"table": "mart.risk_score_df", "columns": ["facility_code", "posts_per_day", "height"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO advisor SELECT * FROM legacy\ncur.execute(\"SELECT price_in_dollars, college_location FROM artist_concerts LIMIT 377\")\n", "labels": {"reads": [{"table": "artist_concerts", "columns": ["price_in_dollars", "college_location"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO militarypersonnel (inspectionscore, development_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "militarypersonnel", "columns": ["inspectionscore", "development_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO landfill_capacity_north_america SELECT max_dissolved_oxygen, avg_usage, city_id FROM london.stations WHERE max_dissolved_oxygen > 420\"\n", "labels": {"reads": [{"table": "london.stations", "columns": ["max_dissolved_oxygen", "avg_usage", "city_id"]}], "writes": [{"table": "landfill_capacity_north_america", "columns": ["max_dissolved_oxygen", "avg_usage", "city_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO rural_infrastructure SELECT contributiondate, request_date, home_team_id, personnel_id FROM community_engagement WHERE contributiondate > 122\"\n", "labels": {"reads": [{"table": "community_engagement", "columns": ["contributiondate", "request_date", "home_team_id", "personnel_id"]}], "writes": [{"table": "rural_infrastructure", "columns": ["contributiondate", "request_date", "home_team_id", "personnel_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO water_consumption SELECT a.material_name, b.court_appearances FROM agroecology_practices a JOIN gene b ON a.hoursspent = b.hoursspent\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": null}, {"table": "gene", "columns": null}], "writes": [{"table": "water_consumption", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table store --target-dir /tmp/land\n", "labels": {"reads": [{"table": "store", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM skincaresales\"\n", "labels": {"reads": [{"table": "skincaresales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO playergamedata (project_id, subject) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "playergamedata", "columns": ["project_id", "subject"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessel_tracking SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_practices_2\").toPandas()\ndf[[\"technology\", \"contributor\"]].to_sql(\"mart_orders_di\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_practices_2", "columns": null}], "writes": [{"table": "mart_orders_di", "columns": ["technology", "contributor"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT isfirstattendee, meter_200 FROM sustainable_practices_2\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"news_stories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sustainable_practices_2", "columns": ["isfirstattendee", "meter_200"]}], "writes": [{"table": "news_stories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO stg.member_point_df SELECT a.operationname, b.player_name FROM constructionlaborstatistics a JOIN diplomacy_events b ON a.amount_settled = b.amount_settled\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "constructionlaborstatistics", "columns": null}, {"table": "diplomacy_events", "columns": null}], "writes": [{"table": "stg.member_point_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO phishing_targets SELECT premises_type, pilot_id, patentexpirationdate, cb_year FROM dw.inventory_delta WHERE premises_type > 129\"\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": ["premises_type", "pilot_id", "patentexpirationdate", "cb_year"]}], "writes": [{"table": "phishing_targets", "columns": ["premises_type", "pilot_id", "patentexpirationdate", "cb_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dp_articles SELECT a.nation, b.factory_id FROM ref_budget_codes a JOIN club b ON a.total_amount = b.total_amount\"\n", "labels": {"reads": [{"table": "ref_budget_codes", "columns": null}, {"table": "club", "columns": null}], "writes": [{"table": "dp_articles", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO item_prices SELECT lieutenant_governor, productiondate FROM vehicledata WHERE lieutenant_governor > 23;\nEOF\n", "labels": {"reads": [{"table": "vehicledata", "columns": ["lieutenant_governor", "productiondate"]}], "writes": [{"table": "item_prices", "columns": ["lieutenant_governor", "productiondate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"purchase\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"customer_addresses\")\n", "labels": {"reads": [{"table": "purchase", "columns": null}], "writes": [{"table": "customer_addresses", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT budget_allocation, person_name FROM wildlife LIMIT 340\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO green_building_projects SELECT steps, rooms FROM classicgame WHERE steps > 218\")\n", "labels": {"reads": [{"table": "wildlife", "columns": ["budget_allocation", "person_name"]}, {"table": "classicgame", "columns": ["steps", "rooms"]}], "writes": [{"table": "green_building_projects", "columns": ["steps", "rooms"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT agency_id, ship_id FROM disaster_response_donations LIMIT 282\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": ["agency_id", "ship_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM shoes\", conn)\ndf.to_sql(\"dws.dws_users_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "shoes", "columns": null}], "writes": [{"table": "dws.dws_users_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO defense_projects (views, access_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "defense_projects", "columns": ["views", "access_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO drug_sales SELECT floor_area_m2, amount_settled, calories FROM documents WHERE floor_area_m2 > 448;\nEOF\n", "labels": {"reads": [{"table": "documents", "columns": ["floor_area_m2", "amount_settled", "calories"]}], "writes": [{"table": "drug_sales", "columns": ["floor_area_m2", "amount_settled", "calories"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table school --columns worker,fault_log_entry_datetime --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "school", "columns": ["worker", "fault_log_entry_datetime"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO nomination SELECT a.product_type, b.enable_dm FROM co2emissions a JOIN yearly_production b ON a.student_name = b.student_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "co2emissions", "columns": null}, {"table": "yearly_production", "columns": null}], "writes": [{"table": "nomination", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nsql = \"INSERT INTO attorneylocationyear SELECT a.healthequitymetricscore, b.customer_number FROM customers a JOIN customer b ON a.cmi_cross_ref_id = b.cmi_cross_ref_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "customers", "columns": null}, {"table": "customer", "columns": null}], "writes": [{"table": "attorneylocationyear", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO renewable_projects SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table atlantic_plate --columns donorid,ship_agent_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "atlantic_plate", "columns": ["donorid", "ship_agent_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teaches\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "teaches", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dws_coupon_use_hourly depends on workshops\ndbt run --select dws_coupon_use_hourly --vars '{\"source_table\":\"workshops\"}'\n", "labels": {"reads": [{"table": "workshops", "columns": null}], "writes": [{"table": "dws_coupon_use_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.coupon_use_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"experts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"forest\")\n", "labels": {"reads": [{"table": "experts", "columns": null}], "writes": [{"table": "forest", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO satisfaction SELECT fuelconsumed, plant, competition_type, last_name FROM ap_budget WHERE fuelconsumed > 66\"\n", "labels": {"reads": [{"table": "ap_budget", "columns": ["fuelconsumed", "plant", "competition_type", "last_name"]}], "writes": [{"table": "satisfaction", "columns": ["fuelconsumed", "plant", "competition_type", "last_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dishes SELECT interest_group, origin_city, postal_code, treatment_type FROM trains WHERE interest_group > 84\"\n", "labels": {"reads": [{"table": "trains", "columns": ["interest_group", "origin_city", "postal_code", "treatment_type"]}], "writes": [{"table": "dishes", "columns": ["interest_group", "origin_city", "postal_code", "treatment_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fund_investments SELECT species_id, dockingdate, max_depth FROM uniteddefense.equipmentsales WHERE species_id > 245\"\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": ["species_id", "dockingdate", "max_depth"]}], "writes": [{"table": "fund_investments", "columns": ["species_id", "dockingdate", "max_depth"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO indian_ocean_wells SELECT a.book_club_id, b.steps FROM film_actor a JOIN dates b ON a.class_section = b.class_section\"\n", "labels": {"reads": [{"table": "film_actor", "columns": null}, {"table": "dates", "columns": null}], "writes": [{"table": "indian_ocean_wells", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT activity_date, meal_date FROM dw.users_hourly LIMIT 96\")\nmetrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO sustainable_urban_properties_2 SELECT location_text, attorney_last_name, shippingmethod, potency FROM bi.member_point_full WHERE location_text > 305\")\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": ["activity_date", "meal_date"]}, {"table": "bi.member_point_full", "columns": ["location_text", "attorney_last_name", "shippingmethod", "potency"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["location_text", "attorney_last_name", "shippingmethod", "potency"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_inventory SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT lot_details, number_deaths FROM iron_ore_production LIMIT 345;\nINSERT INTO researchgrants SELECT framework_id, draft_class, financial_wellbeing_score FROM menu_engineering WHERE framework_id > 484;\nSQL\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["lot_details", "number_deaths"]}, {"table": "menu_engineering", "columns": ["framework_id", "draft_class", "financial_wellbeing_score"]}], "writes": [{"table": "researchgrants", "columns": ["framework_id", "draft_class", "financial_wellbeing_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ll_hours, maintenanceid FROM timber_sales\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"dws.dws_member_point_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "timber_sales", "columns": ["ll_hours", "maintenanceid"]}], "writes": [{"table": "dws.dws_member_point_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO microfinance_clients SELECT inspection_id, quarter, source_u_id FROM waste_production WHERE inspection_id > 190\"\n", "labels": {"reads": [{"table": "waste_production", "columns": ["inspection_id", "quarter", "source_u_id"]}], "writes": [{"table": "microfinance_clients", "columns": ["inspection_id", "quarter", "source_u_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM space_exploration\", conn)\ndf.to_sql(\"singer\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "space_exploration", "columns": null}], "writes": [{"table": "singer", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO subjects SELECT * FROM legacy\ncur.execute(\"SELECT is_commercial, characteristic_name FROM mart.mart_products_hourly LIMIT 324\")\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": ["is_commercial", "characteristic_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recyclingratessouthamerica\").toPandas()\ndf[[\"comptroller\", \"animal_type\"]].to_sql(\"iron\", engine, index=False)\n", "labels": {"reads": [{"table": "recyclingratessouthamerica", "columns": null}], "writes": [{"table": "iron", "columns": ["comptroller", "animal_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.teamname > 62).all()\n# src table: visits_restaurant\nengine.execute(\"INSERT INTO exit_strategy SELECT * FROM visits_restaurant\")\n", "labels": {"reads": [{"table": "visits_restaurant", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT promotionid, num_hotels FROM human_resources LIMIT 375\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ods.ods_coupon_use_di SELECT accident_date, checkout, diagnosis, yield_id FROM assets_frameworks WHERE accident_date > 154\")\n", "labels": {"reads": [{"table": "human_resources", "columns": ["promotionid", "num_hotels"]}, {"table": "assets_frameworks", "columns": ["accident_date", "checkout", "diagnosis", "yield_id"]}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": ["accident_date", "checkout", "diagnosis", "yield_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO cerium_production SELECT velocity, flightid, ssn FROM dws.coupon_use_di WHERE velocity > 153;\nEOF\n", "labels": {"reads": [{"table": "dws.coupon_use_di", "columns": ["velocity", "flightid", "ssn"]}], "writes": [{"table": "cerium_production", "columns": ["velocity", "flightid", "ssn"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO green_buildings SELECT payment_date, merchandise_id, quantitysold FROM food_safety_inspections WHERE payment_date > 133\")\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": ["payment_date", "merchandise_id", "quantitysold"]}], "writes": [{"table": "green_buildings", "columns": ["payment_date", "merchandise_id", "quantitysold"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table manufacturermaterials --columns review_date,media_type_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "manufacturermaterials", "columns": ["review_date", "media_type_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO aid_missions SELECT a.recipient, b.artwork_id FROM street_markets a JOIN port_office b ON a.portname = b.portname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "street_markets", "columns": null}, {"table": "port_office", "columns": null}], "writes": [{"table": "aid_missions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT num_stops, consumer_id FROM militarycyberops LIMIT 247\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["num_stops", "consumer_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"game_sessions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ai_safety_incidents\")\nsrc.write.insertInto(\"ods.ods_users_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": null}], "writes": [{"table": "ods.ods_users_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO claims_documents (market, sales_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "claims_documents", "columns": ["market", "sales_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bridges depends on landfill_capacity\ndbt run --select bridges --vars 'source: landfill_capacity'\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}], "writes": [{"table": "bridges", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO market_trends SELECT purchase_date, installation_date, donationamount FROM accounts WHERE purchase_date > 50;\nEOF\n", "labels": {"reads": [{"table": "accounts", "columns": ["purchase_date", "installation_date", "donationamount"]}], "writes": [{"table": "market_trends", "columns": ["purchase_date", "installation_date", "donationamount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nsql = \"INSERT INTO defense_contracts SELECT a.isfirstattendee, b.consumer_id FROM event_attendance a JOIN team b ON a.archaeologist_name = b.archaeologist_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "event_attendance", "columns": null}, {"table": "team", "columns": null}], "writes": [{"table": "defense_contracts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO equipment (garment_type, review_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "equipment", "columns": ["garment_type", "review_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vessels_2 (schedule_id, program_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vessels_2", "columns": ["schedule_id", "program_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT training_id, labor_hour_id FROM digital_assets LIMIT 416\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "digital_assets", "columns": ["training_id", "labor_hour_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"purchase\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "purchase", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT fault_short_name, underrepresented_community FROM player_college LIMIT 92\")\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO league SELECT community_members, stories, hotel_chain_name FROM food_production WHERE community_members > 79\")\n", "labels": {"reads": [{"table": "player_college", "columns": ["fault_short_name", "underrepresented_community"]}, {"table": "food_production", "columns": ["community_members", "stories", "hotel_chain_name"]}], "writes": [{"table": "league", "columns": ["community_members", "stories", "hotel_chain_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT spacecraftid, campaign FROM org_donation LIMIT 132\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "org_donation", "columns": ["spacecraftid", "campaign"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO auctions SELECT fertilizer_id, date_order_placed FROM navalvessels WHERE fertilizer_id > 394\"\n", "labels": {"reads": [{"table": "navalvessels", "columns": ["fertilizer_id", "date_order_placed"]}], "writes": [{"table": "auctions", "columns": ["fertilizer_id", "date_order_placed"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model region depends on ods.member_point_df\ndbt run --select region --vars '{\"source_table\":\"ods.member_point_df\"}'\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": null}], "writes": [{"table": "region", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT meter_100, cost FROM researchgrants\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"drought_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["meter_100", "cost"]}], "writes": [{"table": "drought_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM gender\"\n", "labels": {"reads": [{"table": "gender", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dw.dw_member_point_di SELECT initiative, loan_amount, driverid, cause_id FROM laborstatistics WHERE initiative > 155;\nEOF\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["initiative", "loan_amount", "driverid", "cause_id"]}], "writes": [{"table": "dw.dw_member_point_di", "columns": ["initiative", "loan_amount", "driverid", "cause_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"ads.ads_cart_item_hourly\")\npersist_to_sink(df, \"bioprocesses\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": null}], "writes": [{"table": "bioprocesses", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO libraries SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tv_shows_genre\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"financialwellbeing\")\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO stg.stg_exposure_di SELECT vaccine_name, timestamp FROM store_district WHERE vaccine_name > 127\")\n", "labels": {"reads": [{"table": "store_district", "columns": ["vaccine_name", "timestamp"]}], "writes": [{"table": "stg.stg_exposure_di", "columns": ["vaccine_name", "timestamp"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"platformh\")\nupsert_to_warehouse(df, \"co2_emissions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "platformh", "columns": null}], "writes": [{"table": "co2_emissions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table player_stats --columns preference_score,ssn --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "player_stats", "columns": ["preference_score", "ssn"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO habitat3 SELECT a.equipment_id, b.annual_entry_exit FROM soil_moisture a JOIN org_volunteer b ON a.building_address = b.building_address\"\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}, {"table": "org_volunteer", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO enzyme SELECT long, rank, fieldid FROM attendee_demographics WHERE long > 94;\nEOF\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": ["long", "rank", "fieldid"]}], "writes": [{"table": "enzyme", "columns": ["long", "rank", "fieldid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO organization SELECT professional_development, labordate, part_name FROM royal_family WHERE professional_development > 464\"\n", "labels": {"reads": [{"table": "royal_family", "columns": ["professional_development", "labordate", "part_name"]}], "writes": [{"table": "organization", "columns": ["professional_development", "labordate", "part_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi_device_log_daily\")\nsrc.write.insertInto(\"genre_songs\", overwrite=True)\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": null}], "writes": [{"table": "genre_songs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_shipments_hourly SELECT * FROM legacy\ncur.execute(\"SELECT customer, extraction_amount FROM missions LIMIT 12\")\n", "labels": {"reads": [{"table": "missions", "columns": ["customer", "extraction_amount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.flno > 420).all()\n# src table: ods.shipments_df\nengine.execute(\"INSERT INTO dws.dws_events_hourly SELECT * FROM ods.shipments_df\")\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}], "writes": [{"table": "dws.dws_events_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO participates_in (maxoccupancy, budget_in_billions) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "participates_in", "columns": ["maxoccupancy", "budget_in_billions"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO rooms SELECT post_category, services, period FROM product_details WHERE post_category > 500\"\n", "labels": {"reads": [{"table": "product_details", "columns": ["post_category", "services", "period"]}], "writes": [{"table": "rooms", "columns": ["post_category", "services", "period"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"esports_teams\")\nsrc.write.insertInto(\"crop_yield\", overwrite=True)\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}], "writes": [{"table": "crop_yield", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"media_library\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "media_library", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT relationship, plant_name FROM phone LIMIT 259\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "phone", "columns": ["relationship", "plant_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO contract_timeline (brand_id, fabric_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "contract_timeline", "columns": ["brand_id", "fabric_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.entrydate > 277).all()\n# src table: ocean_acidity\nengine.execute(\"INSERT INTO economic_diversification_efforts SELECT * FROM ocean_acidity\")\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": null}], "writes": [{"table": "economic_diversification_efforts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"accounts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"az_drought_impact\")\n", "labels": {"reads": [{"table": "accounts", "columns": null}], "writes": [{"table": "az_drought_impact", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO procedures SELECT partner, physician, animal_type, day_of_week FROM mining_companies WHERE partner > 267\"\n", "labels": {"reads": [{"table": "mining_companies", "columns": ["partner", "physician", "animal_type", "day_of_week"]}], "writes": [{"table": "procedures", "columns": ["partner", "physician", "animal_type", "day_of_week"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table concert_revenue --columns course_name,offender_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "concert_revenue", "columns": ["course_name", "offender_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ods.campaigns_di --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.campaigns_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO member SELECT year_built, union_members, building_address, categoryid FROM biotech_startups WHERE year_built > 467\"\n", "labels": {"reads": [{"table": "biotech_startups", "columns": ["year_built", "union_members", "building_address", "categoryid"]}], "writes": [{"table": "member", "columns": ["year_built", "union_members", "building_address", "categoryid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT advisory_id, taxi_model FROM cuisine LIMIT 247\")\nimport logging\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO songs SELECT projectid, initiative_id FROM product_details WHERE projectid > 348\")\n", "labels": {"reads": [{"table": "cuisine", "columns": ["advisory_id", "taxi_model"]}, {"table": "product_details", "columns": ["projectid", "initiative_id"]}], "writes": [{"table": "songs", "columns": ["projectid", "initiative_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bridge SELECT * FROM legacy\ncur.execute(\"SELECT extraction_state, disease FROM commercialbuildings LIMIT 133\")\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": ["extraction_state", "disease"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO manufacturermaterials SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO races SELECT mineral, section_id FROM school_roster WHERE mineral > 325\")\n", "labels": {"reads": [{"table": "school_roster", "columns": ["mineral", "section_id"]}], "writes": [{"table": "races", "columns": ["mineral", "section_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO inclusive_housing SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO recycling_centers SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO construction_union SELECT fare_date, community_size FROM trucks WHERE fare_date > 138\"\n", "labels": {"reads": [{"table": "trucks", "columns": ["fare_date", "community_size"]}], "writes": [{"table": "construction_union", "columns": ["fare_date", "community_size"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO match_season SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.amount_due > 497).all()\n# src table: community_leaders\nengine.execute(\"INSERT INTO renewable_energy_investments SELECT * FROM community_leaders\")\n", "labels": {"reads": [{"table": "community_leaders", "columns": null}], "writes": [{"table": "renewable_energy_investments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cargo_handling (quantity_containers, ngo_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cargo_handling", "columns": ["quantity_containers", "ngo_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT campaign_name, assistingnurse FROM shipments LIMIT 58\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO routes SELECT profession_count, savingsid FROM vessel_registry WHERE profession_count > 177\")\n", "labels": {"reads": [{"table": "shipments", "columns": ["campaign_name", "assistingnurse"]}, {"table": "vessel_registry", "columns": ["profession_count", "savingsid"]}], "writes": [{"table": "routes", "columns": ["profession_count", "savingsid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO pacific_ocean SELECT don_id, astronaut_name, truck_details, thing_id FROM ancient_artifacts WHERE don_id > 236\"], check=True)\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": ["don_id", "astronaut_name", "truck_details", "thing_id"]}], "writes": [{"table": "pacific_ocean", "columns": ["don_id", "astronaut_name", "truck_details", "thing_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO grapes SELECT involved_in_lifelong_learning, foreign, chip_model, amount_donated FROM arctic_research WHERE involved_in_lifelong_learning > 56\"], check=True)\n", "labels": {"reads": [{"table": "arctic_research", "columns": ["involved_in_lifelong_learning", "foreign", "chip_model", "amount_donated"]}], "writes": [{"table": "grapes", "columns": ["involved_in_lifelong_learning", "foreign", "chip_model", "amount_donated"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sustainablepractices > 411).all()\n# src table: human_resources\nengine.execute(\"INSERT INTO police_stations SELECT * FROM human_resources\")\n", "labels": {"reads": [{"table": "human_resources", "columns": null}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table life_expectancy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "life_expectancy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM militaryequipment\"\n", "labels": {"reads": [{"table": "militaryequipment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM diversity\", conn)\ndf.to_sql(\"spacecraft_manufacturers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "diversity", "columns": null}], "writes": [{"table": "spacecraft_manufacturers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO student_addresses SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT clubid, attorney_id FROM vessel_tracking LIMIT 267\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO healthcare_centers SELECT water_type, enable_dm, order_status_code FROM film_category WHERE water_type > 321\")\n", "labels": {"reads": [{"table": "vessel_tracking", "columns": ["clubid", "attorney_id"]}, {"table": "film_category", "columns": ["water_type", "enable_dm", "order_status_code"]}], "writes": [{"table": "healthcare_centers", "columns": ["water_type", "enable_dm", "order_status_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.is_commercial > 95).all()\n# src table: ods.inventory_df\nengine.execute(\"INSERT INTO farmers SELECT * FROM ods.inventory_df\")\n", "labels": {"reads": [{"table": "ods.inventory_df", "columns": null}], "writes": [{"table": "farmers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM courts\", conn)\ndf.to_sql(\"stg.stg_risk_score\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "courts", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO military_technology_projects SELECT transactionid, media_literacy_score, venue FROM fairness_scores WHERE transactionid > 323\"\n", "labels": {"reads": [{"table": "fairness_scores", "columns": ["transactionid", "media_literacy_score", "venue"]}], "writes": [{"table": "military_technology_projects", "columns": ["transactionid", "media_literacy_score", "venue"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"musical\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "musical", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.dwd_member_point_di SELECT a.num_tools, b.org_size FROM investment_accounts a JOIN all_star b ON a.assessment_score = b.assessment_score\"\n", "labels": {"reads": [{"table": "investment_accounts", "columns": null}, {"table": "all_star", "columns": null}], "writes": [{"table": "dwd.dwd_member_point_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO community_education_programs (access_date, energy_star_rating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "community_education_programs", "columns": ["access_date", "energy_star_rating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"construction_labor\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "construction_labor", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"innovation_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "innovation_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO container_ships SELECT a.winning_pilot, b.hoursspent FROM rigs a JOIN ytterbium_supply b ON a.gender_diversity = b.gender_diversity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rigs", "columns": null}, {"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "container_ships", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT launch_agency, healthcareid FROM cybersecurity_strategies LIMIT 77\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "cybersecurity_strategies", "columns": ["launch_agency", "healthcareid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO manufacturer SELECT chip_model, vendor FROM document_sections_images WHERE chip_model > 237\")\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["chip_model", "vendor"]}], "writes": [{"table": "manufacturer", "columns": ["chip_model", "vendor"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_ad_interactions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"course_attendance\")\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": null}], "writes": [{"table": "course_attendance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table airport --target-dir /tmp/land\n", "labels": {"reads": [{"table": "airport", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table producersnewmexico --target-dir /tmp/land\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO emerging_markets.digital_assets SELECT requestdate, customer_status_code FROM flu_cases WHERE requestdate > 60\"\n", "labels": {"reads": [{"table": "flu_cases", "columns": ["requestdate", "customer_status_code"]}], "writes": [{"table": "emerging_markets.digital_assets", "columns": ["requestdate", "customer_status_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO student_mental_health SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO healthcare_budget SELECT a.organizationname, b.precedent_id FROM courts a JOIN dws.dws_risk_score_daily b ON a.count_id = b.count_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "courts", "columns": null}, {"table": "dws.dws_risk_score_daily", "columns": null}], "writes": [{"table": "healthcare_budget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.dwd_risk_score_delta (staff_id, family_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": ["staff_id", "family_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT thefttypeid, vr_platform FROM evsales LIMIT 255\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO artifact_analysis SELECT yearadded, content_type FROM gold WHERE yearadded > 400\")\n", "labels": {"reads": [{"table": "evsales", "columns": ["thefttypeid", "vr_platform"]}, {"table": "gold", "columns": ["yearadded", "content_type"]}], "writes": [{"table": "artifact_analysis", "columns": ["yearadded", "content_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.unavailable > 30).all()\n# src table: spacecraftspeed\nengine.execute(\"INSERT INTO submission SELECT * FROM spacecraftspeed\")\n", "labels": {"reads": [{"table": "spacecraftspeed", "columns": null}], "writes": [{"table": "submission", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO construction_labor SELECT fairtrade, document_id, white FROM australia_offset_programs WHERE fairtrade > 282;\nEOF\n", "labels": {"reads": [{"table": "australia_offset_programs", "columns": ["fairtrade", "document_id", "white"]}], "writes": [{"table": "construction_labor", "columns": ["fairtrade", "document_id", "white"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO seal_population SELECT dependent_name, roomtype FROM weather WHERE dependent_name > 191\"\n", "labels": {"reads": [{"table": "weather", "columns": ["dependent_name", "roomtype"]}], "writes": [{"table": "seal_population", "columns": ["dependent_name", "roomtype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_safety_inspections\").toPandas()\ndf[[\"inspectionscore\", \"onscholarship\"]].to_sql(\"regional_railways\", engine, index=False)\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": null}], "writes": [{"table": "regional_railways", "columns": ["inspectionscore", "onscholarship"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO musicsales (local_authority, participation_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "musicsales", "columns": ["local_authority", "participation_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO team_revenue SELECT number_of_sightings, dish_name, exit_type, catalog_name FROM militaryequipmentsales WHERE number_of_sightings > 295\"\n", "labels": {"reads": [{"table": "militaryequipmentsales", "columns": ["number_of_sightings", "dish_name", "exit_type", "catalog_name"]}], "writes": [{"table": "team_revenue", "columns": ["number_of_sightings", "dish_name", "exit_type", "catalog_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO therapists SELECT totalprice, subject_id, form_id, element_id FROM disaster_zones WHERE totalprice > 36\"], check=True)\n", "labels": {"reads": [{"table": "disaster_zones", "columns": ["totalprice", "subject_id", "form_id", "element_id"]}], "writes": [{"table": "therapists", "columns": ["totalprice", "subject_id", "form_id", "element_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"station_company\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ods.campaigns_di\")\n", "labels": {"reads": [{"table": "station_company", "columns": null}], "writes": [{"table": "ods.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO providers (safety_score, style) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "providers", "columns": ["safety_score", "style"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO device SELECT dockingdate, uses_vr, founder_lgbtq, open_year FROM tourism_activities WHERE dockingdate > 15\"\n", "labels": {"reads": [{"table": "tourism_activities", "columns": ["dockingdate", "uses_vr", "founder_lgbtq", "open_year"]}], "writes": [{"table": "device", "columns": ["dockingdate", "uses_vr", "founder_lgbtq", "open_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.mart_users (cost, incident_count) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_users", "columns": ["cost", "incident_count"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safetytests\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"habitat\")\n", "labels": {"reads": [{"table": "safetytests", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO emissions SELECT a.played, b.mining_operation_id FROM dwd.products_hourly a JOIN underwater_trenches b ON a.artpiecename = b.artpiecename\"\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": null}, {"table": "underwater_trenches", "columns": null}], "writes": [{"table": "emissions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO vehicle_data SELECT workerid, complaint_status_code, impact_id FROM athlete_wellbeing WHERE workerid > 108\")\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["workerid", "complaint_status_code", "impact_id"]}], "writes": [{"table": "vehicle_data", "columns": ["workerid", "complaint_status_code", "impact_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"legal_aid_providers\").toPandas()\ndf[[\"shipmentid\", \"start_therapy\"]].to_sql(\"section\", engine, index=False)\n", "labels": {"reads": [{"table": "legal_aid_providers", "columns": null}], "writes": [{"table": "section", "columns": ["shipmentid", "start_therapy"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO iron (neighborhoodid, elevation) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "iron", "columns": ["neighborhoodid", "elevation"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table train_maintenance --columns floor_area_m2,artpiecename --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "train_maintenance", "columns": ["floor_area_m2", "artpiecename"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO rent_arrears (production_rate, used_kb) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "rent_arrears", "columns": ["production_rate", "used_kb"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthydelights\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"editor\")\n", "labels": {"reads": [{"table": "healthydelights", "columns": null}], "writes": [{"table": "editor", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT shipped_to, practiceid FROM paper_data LIMIT 34;\nINSERT INTO menu_vendors SELECT grade, shelter_id FROM articles_es WHERE grade > 343;\nSQL\n", "labels": {"reads": [{"table": "paper_data", "columns": ["shipped_to", "practiceid"]}, {"table": "articles_es", "columns": ["grade", "shelter_id"]}], "writes": [{"table": "menu_vendors", "columns": ["grade", "shelter_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dishid, dish_type FROM mart.mart_campaigns_daily\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"waste_generation_metrics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": ["dishid", "dish_type"]}], "writes": [{"table": "waste_generation_metrics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO public_transport.passenger_count SELECT warehouse_state, yield_per_acre, refugee_name, customer_address FROM reservoirs WHERE warehouse_state > 113\")\n", "labels": {"reads": [{"table": "reservoirs", "columns": ["warehouse_state", "yield_per_acre", "refugee_name", "customer_address"]}], "writes": [{"table": "public_transport.passenger_count", "columns": ["warehouse_state", "yield_per_acre", "refugee_name", "customer_address"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model west_providers depends on dwd_payments_delta\ndbt build -s west_providers --vars 'source: dwd_payments_delta'\n", "labels": {"reads": [{"table": "dwd_payments_delta", "columns": null}], "writes": [{"table": "west_providers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM restaurant_revenue\"\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_orders_di\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"eco_hotels\")\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": null}], "writes": [{"table": "eco_hotels", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table global_sales_2022 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO emergency_categories (operation_id, birth_place) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "emergency_categories", "columns": ["operation_id", "birth_place"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO certifications SELECT a.appointment_duration, b.blockfloor FROM emergency_responses a JOIN mart.mart_refunds_hourly b ON a.mascot = b.mascot\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "emergency_responses", "columns": null}, {"table": "mart.mart_refunds_hourly", "columns": null}], "writes": [{"table": "certifications", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT claim_header_id, route_id FROM ai_safety_papers2 LIMIT 100\")\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO activity SELECT amenid, media_type_id FROM systems WHERE amenid > 23\")\n", "labels": {"reads": [{"table": "ai_safety_papers2", "columns": ["claim_header_id", "route_id"]}, {"table": "systems", "columns": ["amenid", "media_type_id"]}], "writes": [{"table": "activity", "columns": ["amenid", "media_type_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"site\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"researchprojects\")\n", "labels": {"reads": [{"table": "site", "columns": null}], "writes": [{"table": "researchprojects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO agriculturalinnovations (feedtype, ties) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "agriculturalinnovations", "columns": ["feedtype", "ties"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ai_ethics SELECT major, gameid FROM songs WHERE major > 33\"\n", "labels": {"reads": [{"table": "songs", "columns": ["major", "gameid"]}], "writes": [{"table": "ai_ethics", "columns": ["major", "gameid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model haircare_cruelty depends on climate_communication\ndbt run -s haircare_cruelty --vars '{\"src\":\"climate_communication\"}'\n", "labels": {"reads": [{"table": "climate_communication", "columns": null}], "writes": [{"table": "haircare_cruelty", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ocean_health_monitor\")\nsrc.write.insertInto(\"whale_sharks\", overwrite=True)\n", "labels": {"reads": [{"table": "ocean_health_monitor", "columns": null}], "writes": [{"table": "whale_sharks", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.sessions_daily SELECT * FROM legacy\ncur.execute(\"SELECT ad_id, deliverydate FROM eco_diversification_investment LIMIT 305\")\n", "labels": {"reads": [{"table": "eco_diversification_investment", "columns": ["ad_id", "deliverydate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT planned_delivery_date, fault_log_entry_datetime FROM marine_species_status LIMIT 379\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "marine_species_status", "columns": ["planned_delivery_date", "fault_log_entry_datetime"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model album depends on animal_species\ndbt build -s album --vars 'source: animal_species'\n", "labels": {"reads": [{"table": "animal_species", "columns": null}], "writes": [{"table": "album", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.agegroup > 91).all()\n# src table: diversity_metrics\nengine.execute(\"INSERT INTO programoutcomes SELECT * FROM diversity_metrics\")\n", "labels": {"reads": [{"table": "diversity_metrics", "columns": null}], "writes": [{"table": "programoutcomes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"performers\").toPandas()\ndf[[\"shipment_date\", \"mental_health_score\"]].to_sql(\"artcontributors\", engine, index=False)\n", "labels": {"reads": [{"table": "performers", "columns": null}], "writes": [{"table": "artcontributors", "columns": ["shipment_date", "mental_health_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO ods_cart_item_df SELECT claim_status_description, organisation_type_description FROM arrivals WHERE claim_status_description > 236\")\n", "labels": {"reads": [{"table": "arrivals", "columns": ["claim_status_description", "organisation_type_description"]}], "writes": [{"table": "ods_cart_item_df", "columns": ["claim_status_description", "organisation_type_description"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT journal, diplomacy_id FROM caribbeansea\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"genetics_stats.research_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["journal", "diplomacy_id"]}], "writes": [{"table": "genetics_stats.research_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO circular_economy_initiatives SELECT visitor_count, number_of_platforms FROM bank_info WHERE visitor_count > 274\"\n", "labels": {"reads": [{"table": "bank_info", "columns": ["visitor_count", "number_of_platforms"]}], "writes": [{"table": "circular_economy_initiatives", "columns": ["visitor_count", "number_of_platforms"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tourist_attractions SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO healthbudget SELECT mission_count, class_president_vote, dphone, led_by FROM fossil_fuel_vehicles WHERE mission_count > 128\"\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": ["mission_count", "class_president_vote", "dphone", "led_by"]}], "writes": [{"table": "healthbudget", "columns": ["mission_count", "class_president_vote", "dphone", "led_by"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nimport logging\nsql = \"INSERT INTO document_structures SELECT a.client_first_name, b.train_type FROM esportsteamsafrica a JOIN wastegeneration b ON a.stockid = b.stockid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "esportsteamsafrica", "columns": null}, {"table": "wastegeneration", "columns": null}], "writes": [{"table": "document_structures", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"item_inventory\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads.ads_exposure_di\")\n", "labels": {"reads": [{"table": "item_inventory", "columns": null}], "writes": [{"table": "ads.ads_exposure_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ingredient\")\nsrc.write.insertInto(\"sectors\", overwrite=True)\n", "labels": {"reads": [{"table": "ingredient", "columns": null}], "writes": [{"table": "sectors", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM midwest_materials\", conn)\ndf.to_sql(\"roles\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "midwest_materials", "columns": null}], "writes": [{"table": "roles", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO flu_shots SELECT * FROM legacy\ncur.execute(\"SELECT producerid, energy_efficiency_savings FROM ods.ods_users_di LIMIT 312\")\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": ["producerid", "energy_efficiency_savings"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM yttrium_production\"\n", "labels": {"reads": [{"table": "yttrium_production", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"charging_stations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "charging_stations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table circular_economy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "circular_economy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"genre_songs\").toPandas()\ndf[[\"time_stamp\", \"meter_200\"]].to_sql(\"bank\", engine, index=False)\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}], "writes": [{"table": "bank", "columns": ["time_stamp", "meter_200"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO reviews SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT fare_id, founding_year FROM customersregion LIMIT 281;\nINSERT INTO ingredients SELECT camera_lens_id, museumname, poll_source FROM whale_sharks WHERE camera_lens_id > 245;\nSQL\n", "labels": {"reads": [{"table": "customersregion", "columns": ["fare_id", "founding_year"]}, {"table": "whale_sharks", "columns": ["camera_lens_id", "museumname", "poll_source"]}], "writes": [{"table": "ingredients", "columns": ["camera_lens_id", "museumname", "poll_source"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ref_detention_type\"\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO satellites_by_country SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"waste_data\").toPandas()\ndf[[\"appointment_time\", \"farm_name\"]].to_sql(\"climate_adaptation_re\", engine, index=False)\n", "labels": {"reads": [{"table": "waste_data", "columns": null}], "writes": [{"table": "climate_adaptation_re", "columns": ["appointment_time", "farm_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"foodaid\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"community_health_workers\")\n", "labels": {"reads": [{"table": "foodaid", "columns": null}], "writes": [{"table": "community_health_workers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO communitydevelopment SELECT * FROM legacy\ncur.execute(\"SELECT production_date, workout_type FROM has_allergy LIMIT 279\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["production_date", "workout_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT class, timestamp FROM threat_severity LIMIT 114\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "threat_severity", "columns": ["class", "timestamp"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO international_visitors SELECT * FROM legacy\ncur.execute(\"SELECT cloud_cover, workshop_id FROM mart.mart_campaigns_daily LIMIT 155\")\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": ["cloud_cover", "workshop_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO media_library SELECT a.sustainability_certified, b.awayteamid FROM streams a JOIN bi.bi_campaigns_daily b ON a.amount_due = b.amount_due\"\n", "labels": {"reads": [{"table": "streams", "columns": null}, {"table": "bi.bi_campaigns_daily", "columns": null}], "writes": [{"table": "media_library", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table recycling_rates_state --target-dir /tmp/land\n", "labels": {"reads": [{"table": "recycling_rates_state", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"patient_outcomes\")\nsrc.write.insertInto(\"num_employees\", overwrite=True)\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": null}], "writes": [{"table": "num_employees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"digital_divide_initiatives\")\nexport_to_warehouse(df, \"disinformation_detection\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": null}], "writes": [{"table": "disinformation_detection", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"autoshow\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"camera_lens\")\n", "labels": {"reads": [{"table": "autoshow", "columns": null}], "writes": [{"table": "camera_lens", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"total_capacity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bustrips\")\n", "labels": {"reads": [{"table": "total_capacity", "columns": null}], "writes": [{"table": "bustrips", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO artsheritage (sale_quantity, equipment_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "artsheritage", "columns": ["sale_quantity", "equipment_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO team_revenue SELECT staystart, factory FROM fieldd_info WHERE staystart > 484\")\n", "labels": {"reads": [{"table": "fieldd_info", "columns": ["staystart", "factory"]}], "writes": [{"table": "team_revenue", "columns": ["staystart", "factory"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"local_impact_japan\").toPandas()\ndf[[\"sale_year\", \"maintenance_date\"]].to_sql(\"healthcare_facilities\", engine, index=False)\n", "labels": {"reads": [{"table": "local_impact_japan", "columns": null}], "writes": [{"table": "healthcare_facilities", "columns": ["sale_year", "maintenance_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO wind_farms SELECT museum_name, asessment_outcome_code, grant_date, years_operating FROM crime_stats WHERE museum_name > 470\"\n", "labels": {"reads": [{"table": "crime_stats", "columns": ["museum_name", "asessment_outcome_code", "grant_date", "years_operating"]}], "writes": [{"table": "wind_farms", "columns": ["museum_name", "asessment_outcome_code", "grant_date", "years_operating"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.component_type > 361).all()\n# src table: ads.orders\nengine.execute(\"INSERT INTO journal SELECT * FROM ads.orders\")\n", "labels": {"reads": [{"table": "ads.orders", "columns": null}], "writes": [{"table": "journal", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO organic_cosmetics SELECT airport, continent FROM tv_shows_genre WHERE airport > 207\"\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": ["airport", "continent"]}], "writes": [{"table": "organic_cosmetics", "columns": ["airport", "continent"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO circuits SELECT gamepreference, game_genre, carrier, hourlyrate FROM virtual_tourism WHERE gamepreference > 93\")\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["gamepreference", "game_genre", "carrier", "hourlyrate"]}], "writes": [{"table": "circuits", "columns": ["gamepreference", "game_genre", "carrier", "hourlyrate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"unionmembers\").toPandas()\ndf[[\"plant_location\", \"workoutname\"]].to_sql(\"fleet\", engine, index=False)\n", "labels": {"reads": [{"table": "unionmembers", "columns": null}], "writes": [{"table": "fleet", "columns": ["plant_location", "workoutname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO farm_competition SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO reviews SELECT judge_state, cust_name, building_phone, event_attendance FROM factory_connections WHERE judge_state > 140\"], check=True)\n", "labels": {"reads": [{"table": "factory_connections", "columns": ["judge_state", "cust_name", "building_phone", "event_attendance"]}], "writes": [{"table": "reviews", "columns": ["judge_state", "cust_name", "building_phone", "event_attendance"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT inventor_name, quarter FROM restaurants LIMIT 53;\nINSERT INTO public.police_calls SELECT home_team_three_point, comment_count, certification FROM grad_students WHERE home_team_three_point > 291;\nSQL\n", "labels": {"reads": [{"table": "restaurants", "columns": ["inventor_name", "quarter"]}, {"table": "grad_students", "columns": ["home_team_three_point", "comment_count", "certification"]}], "writes": [{"table": "public.police_calls", "columns": ["home_team_three_point", "comment_count", "certification"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO marketing_budgets SELECT hometeam, emergency_type, room_number FROM mines WHERE hometeam > 216\"\n", "labels": {"reads": [{"table": "mines", "columns": ["hometeam", "emergency_type", "room_number"]}], "writes": [{"table": "marketing_budgets", "columns": ["hometeam", "emergency_type", "room_number"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marine_species_indian SELECT a.has_disability, b.advocate_id FROM ads.ads_campaigns_full a JOIN ingredient_sourcing b ON a.session_date = b.session_date\"\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": null}, {"table": "ingredient_sourcing", "columns": null}], "writes": [{"table": "marine_species_indian", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT strainname, machinery_id FROM disease_prevalence LIMIT 163;\nINSERT INTO arcticocean SELECT doctor_id, category, relationship, other_details FROM pilot_record WHERE doctor_id > 397;\nSQL\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": ["strainname", "machinery_id"]}, {"table": "pilot_record", "columns": ["doctor_id", "category", "relationship", "other_details"]}], "writes": [{"table": "arcticocean", "columns": ["doctor_id", "category", "relationship", "other_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM geneva_motor_show\", conn)\ndf.to_sql(\"ucl_top10\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "geneva_motor_show", "columns": null}], "writes": [{"table": "ucl_top10", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sensor_type, mission_date FROM product_info LIMIT 366;\nINSERT INTO freshwaterfinfish SELECT extraction_date, case_burden, contract_address, refugee_id FROM paintings WHERE extraction_date > 38;\nSQL\n", "labels": {"reads": [{"table": "product_info", "columns": ["sensor_type", "mission_date"]}, {"table": "paintings", "columns": ["extraction_date", "case_burden", "contract_address", "refugee_id"]}], "writes": [{"table": "freshwaterfinfish", "columns": ["extraction_date", "case_burden", "contract_address", "refugee_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO wearable_metrics SELECT * FROM legacy\ncur.execute(\"SELECT mine_location, product_category_code FROM dw.dw_inventory_df LIMIT 404\")\n", "labels": {"reads": [{"table": "dw.dw_inventory_df", "columns": ["mine_location", "product_category_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table restaurants --target-dir /tmp/land\n", "labels": {"reads": [{"table": "restaurants", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mine_workforce\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads_refunds_full\")\n", "labels": {"reads": [{"table": "mine_workforce", "columns": null}], "writes": [{"table": "ads_refunds_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO country_landfill_capacity (bedroom_count, engagementid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "country_landfill_capacity", "columns": ["bedroom_count", "engagementid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO green_building_projects SELECT gymnast_id, local_authority FROM average WHERE gymnast_id > 98\"], check=True)\n", "labels": {"reads": [{"table": "average", "columns": ["gymnast_id", "local_authority"]}], "writes": [{"table": "green_building_projects", "columns": ["gymnast_id", "local_authority"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO coral_reefs SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fiscal_year, coowner_name FROM extraction_methods LIMIT 423\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "extraction_methods", "columns": ["fiscal_year", "coowner_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO russia_nato_diplomacy SELECT publish_date, nominee FROM renewableenergy WHERE publish_date > 442\"\n", "labels": {"reads": [{"table": "renewableenergy", "columns": ["publish_date", "nominee"]}], "writes": [{"table": "russia_nato_diplomacy", "columns": ["publish_date", "nominee"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi_products SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO nba_games SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO workshop SELECT environmental_impact, restypedescription, player_api_id FROM affordablehousing WHERE environmental_impact > 447\"\n", "labels": {"reads": [{"table": "affordablehousing", "columns": ["environmental_impact", "restypedescription", "player_api_id"]}], "writes": [{"table": "workshop", "columns": ["environmental_impact", "restypedescription", "player_api_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO available_policies SELECT emp_id, mine_name, appelation, genrename FROM dws.dws_shipments_full WHERE emp_id > 464\")\n", "labels": {"reads": [{"table": "dws.dws_shipments_full", "columns": ["emp_id", "mine_name", "appelation", "genrename"]}], "writes": [{"table": "available_policies", "columns": ["emp_id", "mine_name", "appelation", "genrename"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT foreign, min_dew_point_f FROM people_addresses\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"dwd.dwd_products\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "people_addresses", "columns": ["foreign", "min_dew_point_f"]}], "writes": [{"table": "dwd.dwd_products", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model player_f depends on mart.mart_vendors\ndbt run --select player_f --vars 'source: mart.mart_vendors'\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": null}], "writes": [{"table": "player_f", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.club_name > 137).all()\n# src table: dwd_risk_score_hourly\nengine.execute(\"INSERT INTO minor_in SELECT * FROM dwd_risk_score_hourly\")\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": null}], "writes": [{"table": "minor_in", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT founder_ethnicity, energy_star_rating FROM laborstatistics LIMIT 352\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["founder_ethnicity", "energy_star_rating"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO player_coach (attorney_id, inspection_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "player_coach", "columns": ["attorney_id", "inspection_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model countries depends on dw_member_point_full\ndbt run --select countries --vars '{\"source_table\":\"dw_member_point_full\"}'\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": null}], "writes": [{"table": "countries", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods.ods_coupon_use_delta\"\n", "labels": {"reads": [{"table": "ods.ods_coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT crispr_id, squadron FROM book_club\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"seamounts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "book_club", "columns": ["crispr_id", "squadron"]}], "writes": [{"table": "seamounts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO wastewatertreatment SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO customer_events SELECT num_pallets, volunteer_hours FROM clinical_trials WHERE num_pallets > 326\"], check=True)\n", "labels": {"reads": [{"table": "clinical_trials", "columns": ["num_pallets", "volunteer_hours"]}], "writes": [{"table": "customer_events", "columns": ["num_pallets", "volunteer_hours"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO exhibitionattendance SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO railway (sqft, numcases) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "railway", "columns": ["sqft", "numcases"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table product_reviews --target-dir /tmp/land\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM emergency_responses\"\n", "labels": {"reads": [{"table": "emergency_responses", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO artifactanalysis SELECT district_name, trip_type, tree_id FROM menu_categories WHERE district_name > 499\"\n", "labels": {"reads": [{"table": "menu_categories", "columns": ["district_name", "trip_type", "tree_id"]}], "writes": [{"table": "artifactanalysis", "columns": ["district_name", "trip_type", "tree_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO organization (fare, eliminated_by) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "organization", "columns": ["fare", "eliminated_by"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model concert_sales depends on archaeologists\ndbt run --select concert_sales --vars 'source: archaeologists'\n", "labels": {"reads": [{"table": "archaeologists", "columns": null}], "writes": [{"table": "concert_sales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"ocean\")\npersist_to_output(df, \"ads.ads_cart_item_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ocean", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.vendors_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.vendors_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO volunteerhours SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stories SELECT policy, screening FROM hotel_chains WHERE policy > 305\"], check=True)\n", "labels": {"reads": [{"table": "hotel_chains", "columns": ["policy", "screening"]}], "writes": [{"table": "stories", "columns": ["policy", "screening"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"dorm\")\nexport_to_output(df, \"artists_valuation\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": [{"table": "artists_valuation", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO patient_satisfaction SELECT union_members, neighborhoodname, meal_date, is_hybrid FROM conservation_programs WHERE union_members > 115\"\n", "labels": {"reads": [{"table": "conservation_programs", "columns": ["union_members", "neighborhoodname", "meal_date", "is_hybrid"]}], "writes": [{"table": "patient_satisfaction", "columns": ["union_members", "neighborhoodname", "meal_date", "is_hybrid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO sites_me SELECT purchase_details, typical_buying_price, flno FROM landfill_capacity_north_america WHERE purchase_details > 472\"], check=True)\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": ["purchase_details", "typical_buying_price", "flno"]}], "writes": [{"table": "sites_me", "columns": ["purchase_details", "typical_buying_price", "flno"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO artsales SELECT a.building_description, b.state_id FROM timber_sales a JOIN dwd.dwd_payments_di b ON a.document_type_description = b.document_type_description\"\n", "labels": {"reads": [{"table": "timber_sales", "columns": null}, {"table": "dwd.dwd_payments_di", "columns": null}], "writes": [{"table": "artsales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO ads.ads_events_df SELECT text_of_notes, matchdate, policy_number FROM dwd.vendors WHERE text_of_notes > 196\"\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": ["text_of_notes", "matchdate", "policy_number"]}], "writes": [{"table": "ads.ads_events_df", "columns": ["text_of_notes", "matchdate", "policy_number"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM class\"\n", "labels": {"reads": [{"table": "class", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model military_spending depends on genre_songs\ndbt run --models military_spending --vars '{\"source_table\":\"genre_songs\"}'\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}], "writes": [{"table": "military_spending", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM coal\", conn)\ndf.to_sql(\"dws.dws_coupon_use_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "coal", "columns": null}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table farmers_india --target-dir /tmp/land\n", "labels": {"reads": [{"table": "farmers_india", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO open_pedagogy_exam (traveler_id, assets) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "open_pedagogy_exam", "columns": ["traveler_id", "assets"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table carbon_offsets --target-dir /tmp/land\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO undergoes SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO timbersales SELECT permitid, license_type, observation_id, production_id FROM atlantic_plate WHERE permitid > 343\")\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": ["permitid", "license_type", "observation_id", "production_id"]}], "writes": [{"table": "timbersales", "columns": ["permitid", "license_type", "observation_id", "production_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cars SELECT build_date, recycler_id, donationyear, count_date FROM problem_log WHERE build_date > 327\"\n", "labels": {"reads": [{"table": "problem_log", "columns": ["build_date", "recycler_id", "donationyear", "count_date"]}], "writes": [{"table": "cars", "columns": ["build_date", "recycler_id", "donationyear", "count_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sustainable_fabrics SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model cosmetics depends on dwd.dwd_campaigns_df\ndbt run --select cosmetics --vars '{\"src\":\"dwd.dwd_campaigns_df\"}'\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": null}], "writes": [{"table": "cosmetics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"dws.dws_risk_score_daily\")\nsave_to_store(df, \"communitydevelopment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_risk_score_daily", "columns": null}], "writes": [{"table": "communitydevelopment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO athletes SELECT serve_id, address, destruction_authorised_by_employee_id, male_id FROM doctors WHERE serve_id > 208\")\n", "labels": {"reads": [{"table": "doctors", "columns": ["serve_id", "address", "destruction_authorised_by_employee_id", "male_id"]}], "writes": [{"table": "athletes", "columns": ["serve_id", "address", "destruction_authorised_by_employee_id", "male_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO rainfall_data SELECT * FROM legacy\ncur.execute(\"SELECT forest_type, rid FROM staff_roles LIMIT 445\")\n", "labels": {"reads": [{"table": "staff_roles", "columns": ["forest_type", "rid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"dwd.dwd_coupon_use_df\")\npersist_to_sink(df, \"vehicle_registrations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_coupon_use_df", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ai_ethics_policies\").toPandas()\ndf[[\"guest_last_name\", \"mining_operation\"]].to_sql(\"nyc_subway\", engine, index=False)\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": null}], "writes": [{"table": "nyc_subway", "columns": ["guest_last_name", "mining_operation"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO drama_workshop_groups SELECT onscholarship, warehouse_id, strat_name, hub_id FROM funding_rounds WHERE onscholarship > 369\"\n", "labels": {"reads": [{"table": "funding_rounds", "columns": ["onscholarship", "warehouse_id", "strat_name", "hub_id"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["onscholarship", "warehouse_id", "strat_name", "hub_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT region_code, organizationname FROM safetyincidents LIMIT 265;\nINSERT INTO musicgenre SELECT co2_reduction_tons, menuitemname, organization_details, tour_name FROM mealtypes WHERE co2_reduction_tons > 281;\nSQL\n", "labels": {"reads": [{"table": "safetyincidents", "columns": ["region_code", "organizationname"]}, {"table": "mealtypes", "columns": ["co2_reduction_tons", "menuitemname", "organization_details", "tour_name"]}], "writes": [{"table": "musicgenre", "columns": ["co2_reduction_tons", "menuitemname", "organization_details", "tour_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.events_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"electricvehicleadoption\")\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO defense_project_timelines SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO therapy_attendance SELECT * FROM legacy\ncur.execute(\"SELECT initiative_name, missions FROM sites LIMIT 120\")\n", "labels": {"reads": [{"table": "sites", "columns": ["initiative_name", "missions"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table efforts --columns strategy_id,moisture_level --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "efforts", "columns": ["strategy_id", "moisture_level"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO labor_statistics SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"funding_records\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw.dw_coupon_use_daily\")\n", "labels": {"reads": [{"table": "funding_records", "columns": null}], "writes": [{"table": "dw.dw_coupon_use_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO refugee_support SELECT contributor, born_state FROM recreation_centers WHERE contributor > 46\"\n", "labels": {"reads": [{"table": "recreation_centers", "columns": ["contributor", "born_state"]}], "writes": [{"table": "refugee_support", "columns": ["contributor", "born_state"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"price_data\").toPandas()\ndf[[\"total_beds\", \"end_station_name\"]].to_sql(\"lessons\", engine, index=False)\n", "labels": {"reads": [{"table": "price_data", "columns": null}], "writes": [{"table": "lessons", "columns": ["total_beds", "end_station_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"movie\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"timber_production\")\n", "labels": {"reads": [{"table": "movie", "columns": null}], "writes": [{"table": "timber_production", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO oceania_countries SELECT funding_id, hashtags, garment, spacecraft_model FROM zip_codes WHERE funding_id > 272\"], check=True)\n", "labels": {"reads": [{"table": "zip_codes", "columns": ["funding_id", "hashtags", "garment", "spacecraft_model"]}], "writes": [{"table": "oceania_countries", "columns": ["funding_id", "hashtags", "garment", "spacecraft_model"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO artprograms SELECT a.state_id, b.kills FROM members a JOIN status b ON a.therapy_sessions = b.therapy_sessions\"\n", "labels": {"reads": [{"table": "members", "columns": null}, {"table": "status", "columns": null}], "writes": [{"table": "artprograms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO autoshow SELECT rank_in_round, line_name, injury_date FROM cosmetics WHERE rank_in_round > 213;\nEOF\n", "labels": {"reads": [{"table": "cosmetics", "columns": ["rank_in_round", "line_name", "injury_date"]}], "writes": [{"table": "autoshow", "columns": ["rank_in_round", "line_name", "injury_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT unionid, material FROM community_policing_events LIMIT 397;\nINSERT INTO ods.ods_member_point_delta SELECT customer_status_code, pilot_name, pipeline_name FROM ai_papers WHERE customer_status_code > 198;\nSQL\n", "labels": {"reads": [{"table": "community_policing_events", "columns": ["unionid", "material"]}, {"table": "ai_papers", "columns": ["customer_status_code", "pilot_name", "pipeline_name"]}], "writes": [{"table": "ods.ods_member_point_delta", "columns": ["customer_status_code", "pilot_name", "pipeline_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO forestry_practices SELECT * FROM legacy\ncur.execute(\"SELECT seal_species, inspection_time FROM dws.dws_events_hourly LIMIT 271\")\n", "labels": {"reads": [{"table": "dws.dws_events_hourly", "columns": ["seal_species", "inspection_time"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO documents SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table sustainability --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sustainability", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO movie SELECT party, serviceid, certification_name, factory_id FROM watertreatmentplants WHERE party > 31\"\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["party", "serviceid", "certification_name", "factory_id"]}], "writes": [{"table": "movie", "columns": ["party", "serviceid", "certification_name", "factory_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO constructors SELECT a.cmi_cross_ref_id, b.username FROM strainlabresults a JOIN investmentsesg b ON a.range = b.range\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "strainlabresults", "columns": null}, {"table": "investmentsesg", "columns": null}], "writes": [{"table": "constructors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO undergoes SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO team SELECT wildlife_type_id, art_movement, park_id, deliveryid FROM supplier_ethics WHERE wildlife_type_id > 393\")\n", "labels": {"reads": [{"table": "supplier_ethics", "columns": ["wildlife_type_id", "art_movement", "park_id", "deliveryid"]}], "writes": [{"table": "team", "columns": ["wildlife_type_id", "art_movement", "park_id", "deliveryid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO ocean_shipping.cargo SELECT open_date, education_id, milestone, socially_responsible FROM safetyorgs WHERE open_date > 352\"\n", "labels": {"reads": [{"table": "safetyorgs", "columns": ["open_date", "education_id", "milestone", "socially_responsible"]}], "writes": [{"table": "ocean_shipping.cargo", "columns": ["open_date", "education_id", "milestone", "socially_responsible"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT performance_id, mean_temperature_f FROM postseason LIMIT 325\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO hospitals SELECT staystart, decision, task FROM reverselogisticstransactions WHERE staystart > 315\")\n", "labels": {"reads": [{"table": "postseason", "columns": ["performance_id", "mean_temperature_f"]}, {"table": "reverselogisticstransactions", "columns": ["staystart", "decision", "task"]}], "writes": [{"table": "hospitals", "columns": ["staystart", "decision", "task"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT energytype, manager_name FROM sustainable_building LIMIT 340\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "sustainable_building", "columns": ["energytype", "manager_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"russia_nato_diplomacy\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.exposure_df\")\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": [{"table": "dws.exposure_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table ref_detention_type --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM programoutcomes\", conn)\ndf.to_sql(\"facility\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "programoutcomes", "columns": null}], "writes": [{"table": "facility", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"student_addresses\")\npush_to_output(df, \"checking\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "student_addresses", "columns": null}], "writes": [{"table": "checking", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model tb_cases depends on institution\ndbt build -s tb_cases --vars '{\"src\":\"institution\"}'\n", "labels": {"reads": [{"table": "institution", "columns": null}], "writes": [{"table": "tb_cases", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM staff_department_assignments\", conn)\ndf.to_sql(\"fleet_management\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": null}], "writes": [{"table": "fleet_management", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"communityengagement\")\nsrc.write.insertInto(\"community_health_centers\", overwrite=True)\n", "labels": {"reads": [{"table": "communityengagement", "columns": null}], "writes": [{"table": "community_health_centers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.team_id_loser > 433).all()\n# src table: voyages\nengine.execute(\"INSERT INTO ods.clicks_delta SELECT * FROM voyages\")\n", "labels": {"reads": [{"table": "voyages", "columns": null}], "writes": [{"table": "ods.clicks_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM midwest_region\"\n", "labels": {"reads": [{"table": "midwest_region", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cosmetics_sales (distance, mine_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cosmetics_sales", "columns": ["distance", "mine_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"colorado_river_basin\")\nupsert_to_output(df, \"ais\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": null}], "writes": [{"table": "ais", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO cybersecurity_vulnerabilities SELECT a.evaluationid, b.cmi_details FROM teams a JOIN recycling_rates b ON a.roaming_country = b.roaming_country\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "teams", "columns": null}, {"table": "recycling_rates", "columns": null}], "writes": [{"table": "cybersecurity_vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT eia_date, galleryid FROM jp_schema.policy_areas LIMIT 149\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "jp_schema.policy_areas", "columns": ["eia_date", "galleryid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attorney_billing_rates\").toPandas()\ndf[[\"songname\", \"ai_powered_features\"]].to_sql(\"satelliteimagery\", engine, index=False)\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": null}], "writes": [{"table": "satelliteimagery", "columns": ["songname", "ai_powered_features"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fish_purchases SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table viewership --target-dir /tmp/land\n", "labels": {"reads": [{"table": "viewership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO model_fairness SELECT is_false, affirmative, contractor FROM waste_types WHERE is_false > 139\"\n", "labels": {"reads": [{"table": "waste_types", "columns": ["is_false", "affirmative", "contractor"]}], "writes": [{"table": "model_fairness", "columns": ["is_false", "affirmative", "contractor"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_safety_inspections\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO call_volume SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reviewscore, policyid FROM prereq LIMIT 444\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [{"table": "prereq", "columns": ["reviewscore", "policyid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO virtual_tours_oceania SELECT directed_by, longitude, entrydate, movement FROM chargingstations WHERE directed_by > 448\"\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["directed_by", "longitude", "entrydate", "movement"]}], "writes": [{"table": "virtual_tours_oceania", "columns": ["directed_by", "longitude", "entrydate", "movement"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table creative_ai_applications --target-dir /tmp/land\n", "labels": {"reads": [{"table": "creative_ai_applications", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO tryout SELECT city_traffic_speed, fare_amount FROM org_volunteer WHERE city_traffic_speed > 365;\nEOF\n", "labels": {"reads": [{"table": "org_volunteer", "columns": ["city_traffic_speed", "fare_amount"]}], "writes": [{"table": "tryout", "columns": ["city_traffic_speed", "fare_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table sports --columns engagement_date,stocking_density --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "sports", "columns": ["engagement_date", "stocking_density"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.council_tax_id > 123).all()\n# src table: military_innovation\nengine.execute(\"INSERT INTO urban_farms SELECT * FROM military_innovation\")\n", "labels": {"reads": [{"table": "military_innovation", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ods_shipments_df SELECT fan_id, theftdate, impact_id FROM attribute_definitions WHERE fan_id > 33\"\n", "labels": {"reads": [{"table": "attribute_definitions", "columns": ["fan_id", "theftdate", "impact_id"]}], "writes": [{"table": "ods_shipments_df", "columns": ["fan_id", "theftdate", "impact_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"supportprograms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"freshwaterfinfish\")\n", "labels": {"reads": [{"table": "supportprograms", "columns": null}], "writes": [{"table": "freshwaterfinfish", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT patient, extraction_state FROM irrigation_systems LIMIT 89;\nINSERT INTO agencies SELECT login_name, digital_channel, recipient_id, review_text FROM vehicledata WHERE login_name > 298;\nSQL\n", "labels": {"reads": [{"table": "irrigation_systems", "columns": ["patient", "extraction_state"]}, {"table": "vehicledata", "columns": ["login_name", "digital_channel", "recipient_id", "review_text"]}], "writes": [{"table": "agencies", "columns": ["login_name", "digital_channel", "recipient_id", "review_text"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tryout SELECT date_incident_start, missiontype FROM broadband_subscribers WHERE date_incident_start > 134\"\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": ["date_incident_start", "missiontype"]}], "writes": [{"table": "tryout", "columns": ["date_incident_start", "missiontype"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO humanitarian_assistance (hub_id, user_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "humanitarian_assistance", "columns": ["hub_id", "user_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table store_product --target-dir /tmp/land\n", "labels": {"reads": [{"table": "store_product", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM farmer_details\"\n", "labels": {"reads": [{"table": "farmer_details", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO salesperson SELECT * FROM legacy\ncur.execute(\"SELECT prominence, stat_id FROM product_sales LIMIT 243\")\n", "labels": {"reads": [{"table": "product_sales", "columns": ["prominence", "stat_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO marine_species_observations SELECT regionid, stockid, virtual_tour_engagement_time, technique_id FROM shariah_compliant_products WHERE regionid > 427\"\n", "labels": {"reads": [{"table": "shariah_compliant_products", "columns": ["regionid", "stockid", "virtual_tour_engagement_time", "technique_id"]}], "writes": [{"table": "marine_species_observations", "columns": ["regionid", "stockid", "virtual_tour_engagement_time", "technique_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO firestations SELECT president_vote, minename FROM dwd.dwd_orders_daily WHERE president_vote > 482\"\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": ["president_vote", "minename"]}], "writes": [{"table": "firestations", "columns": ["president_vote", "minename"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model county_public_safety depends on whale_sharks\ndbt build --models county_public_safety --vars 'source: whale_sharks'\n", "labels": {"reads": [{"table": "whale_sharks", "columns": null}], "writes": [{"table": "county_public_safety", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT closuredate, issued_date FROM dw.member_point_daily LIMIT 401\")\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO bus_fare_collection SELECT songid, avg_depth, claim_type, sensor_type FROM low_value_contracts WHERE songid > 151\")\n", "labels": {"reads": [{"table": "dw.member_point_daily", "columns": ["closuredate", "issued_date"]}, {"table": "low_value_contracts", "columns": ["songid", "avg_depth", "claim_type", "sensor_type"]}], "writes": [{"table": "bus_fare_collection", "columns": ["songid", "avg_depth", "claim_type", "sensor_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM distributors\", conn)\ndf.to_sql(\"ref_locations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "distributors", "columns": null}], "writes": [{"table": "ref_locations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ref_colors SELECT credits, commanding_officer, race_ethnicity FROM feedback WHERE credits > 406;\nEOF\n", "labels": {"reads": [{"table": "feedback", "columns": ["credits", "commanding_officer", "race_ethnicity"]}], "writes": [{"table": "ref_colors", "columns": ["credits", "commanding_officer", "race_ethnicity"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO urban_agriculture_initiatives SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO bi_orders_daily SELECT enzyme_id, department FROM animal_population_status WHERE enzyme_id > 291;\nEOF\n", "labels": {"reads": [{"table": "animal_population_status", "columns": ["enzyme_id", "department"]}], "writes": [{"table": "bi_orders_daily", "columns": ["enzyme_id", "department"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM apac_hotel_views\"\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO units SELECT employee_name, project_name, carriername, unitsperweek FROM settlements WHERE employee_name > 15;\nEOF\n", "labels": {"reads": [{"table": "settlements", "columns": ["employee_name", "project_name", "carriername", "unitsperweek"]}], "writes": [{"table": "units", "columns": ["employee_name", "project_name", "carriername", "unitsperweek"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"airlines\")\nsrc.write.insertInto(\"book_club\", overwrite=True)\n", "labels": {"reads": [{"table": "airlines", "columns": null}], "writes": [{"table": "book_club", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO humanitarianassistanceoperations SELECT menu_type, access_count, activity_name, theftdate FROM mining.company WHERE menu_type > 493\"\n", "labels": {"reads": [{"table": "mining.company", "columns": ["menu_type", "access_count", "activity_name", "theftdate"]}], "writes": [{"table": "humanitarianassistanceoperations", "columns": ["menu_type", "access_count", "activity_name", "theftdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT stayid, unit_of_measure FROM reservoirs LIMIT 2\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO journal_committee SELECT bus_number, detection_id, customer_type_code, age_group FROM multimodal_trips WHERE bus_number > 4\")\n", "labels": {"reads": [{"table": "reservoirs", "columns": ["stayid", "unit_of_measure"]}, {"table": "multimodal_trips", "columns": ["bus_number", "detection_id", "customer_type_code", "age_group"]}], "writes": [{"table": "journal_committee", "columns": ["bus_number", "detection_id", "customer_type_code", "age_group"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT province, calories FROM ngo_funding LIMIT 330\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "ngo_funding", "columns": ["province", "calories"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO inmates SELECT client_first_name, built, address, handling_date FROM factories WHERE client_first_name > 264\"], check=True)\n", "labels": {"reads": [{"table": "factories", "columns": ["client_first_name", "built", "address", "handling_date"]}], "writes": [{"table": "inmates", "columns": ["client_first_name", "built", "address", "handling_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM trade_history\"\n", "labels": {"reads": [{"table": "trade_history", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table neighborhoods --columns farm_id,heritage_site --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "neighborhoods", "columns": ["farm_id", "heritage_site"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT pname, book_title FROM coffee_prices LIMIT 29\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO vaccinations SELECT detention_type_code, operationtype, trench_id FROM stores_2 WHERE detention_type_code > 103\")\n", "labels": {"reads": [{"table": "coffee_prices", "columns": ["pname", "book_title"]}, {"table": "stores_2", "columns": ["detention_type_code", "operationtype", "trench_id"]}], "writes": [{"table": "vaccinations", "columns": ["detention_type_code", "operationtype", "trench_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"basketball_match\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"machinery\")\n", "labels": {"reads": [{"table": "basketball_match", "columns": null}], "writes": [{"table": "machinery", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table league_x --columns royal_family_details,contract_address --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "league_x", "columns": ["royal_family_details", "contract_address"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO distributors SELECT a.organisation_type_description, b.ocean_name FROM workforce a JOIN athlete_wellbeing b ON a.dec = b.dec\"\n", "labels": {"reads": [{"table": "workforce", "columns": null}, {"table": "athlete_wellbeing", "columns": null}], "writes": [{"table": "distributors", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM gamedesign\", conn)\ndf.to_sql(\"bi.device_log\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "gamedesign", "columns": null}], "writes": [{"table": "bi.device_log", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ytterbiumproduction SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO film_actor SELECT success, date_of_birth, shipment_date FROM space_missions WHERE success > 187\"\n", "labels": {"reads": [{"table": "space_missions", "columns": ["success", "date_of_birth", "shipment_date"]}], "writes": [{"table": "film_actor", "columns": ["success", "date_of_birth", "shipment_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM systems\", conn)\ndf.to_sql(\"membership_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": [{"table": "membership_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.publisher > 143).all()\n# src table: drug_approvals\nengine.execute(\"INSERT INTO animal_populations SELECT * FROM drug_approvals\")\n", "labels": {"reads": [{"table": "drug_approvals", "columns": null}], "writes": [{"table": "animal_populations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agro_regions SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO overwatch_scores SELECT chemical_type, pilot FROM tracklists WHERE chemical_type > 328\"\n", "labels": {"reads": [{"table": "tracklists", "columns": ["chemical_type", "pilot"]}], "writes": [{"table": "overwatch_scores", "columns": ["chemical_type", "pilot"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT investorgender, tier FROM ai_safety LIMIT 289\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO tourism_activities SELECT date_of_ceremony, spent, productcategory FROM gardens WHERE date_of_ceremony > 245\")\n", "labels": {"reads": [{"table": "ai_safety", "columns": ["investorgender", "tier"]}, {"table": "gardens", "columns": ["date_of_ceremony", "spent", "productcategory"]}], "writes": [{"table": "tourism_activities", "columns": ["date_of_ceremony", "spent", "productcategory"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM technology_access\"\n", "labels": {"reads": [{"table": "technology_access", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO workercontactinfo SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO emerging_markets.digital_assets SELECT funding, card_id FROM socially_responsible_loans WHERE funding > 367\"\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": ["funding", "card_id"]}], "writes": [{"table": "emerging_markets.digital_assets", "columns": ["funding", "card_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT temperature, coach_id FROM sourcing LIMIT 18\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "sourcing", "columns": ["temperature", "coach_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT rig_id, sale_quantity FROM production_yearly\", engine)\nif not rows:\n    logger.warning('empty result')\nimport logging\ndf.to_sql(\"viewership\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "production_yearly", "columns": ["rig_id", "sale_quantity"]}], "writes": [{"table": "viewership", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"gamedesigndata\")\nsrc.write.insertInto(\"government_transparency\", overwrite=True)\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": [{"table": "government_transparency", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_campaigns_daily --columns date_of_completion,hiv --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["date_of_completion", "hiv"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model worker_union depends on permit\ndbt build --select worker_union --vars '{\"src\":\"permit\"}'\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": [{"table": "worker_union", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.observation_date > 422).all()\n# src table: brandrevenue\nengine.execute(\"INSERT INTO user_interests SELECT * FROM brandrevenue\")\n", "labels": {"reads": [{"table": "brandrevenue", "columns": null}], "writes": [{"table": "user_interests", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT openingid, phone FROM military_personnel_africa\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"musical\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": ["openingid", "phone"]}], "writes": [{"table": "musical", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy_enrollment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO channel SELECT * FROM legacy\ncur.execute(\"SELECT metric_id, units_owned FROM arcticocean LIMIT 289\")\n", "labels": {"reads": [{"table": "arcticocean", "columns": ["metric_id", "units_owned"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.shipment_tracking_number > 215).all()\n# src table: faculty_participates_in\nengine.execute(\"INSERT INTO yearly_production SELECT * FROM faculty_participates_in\")\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": null}], "writes": [{"table": "yearly_production", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tech_accessibility_funding --columns asset_type,course_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tech_accessibility_funding", "columns": ["asset_type", "course_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.bi_exposure_hourly\")\nsrc.write.insertInto(\"community_centers\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": null}], "writes": [{"table": "community_centers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"county\")\nsrc.write.insertInto(\"league\", overwrite=True)\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "league", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO az_drought_impact SELECT incidents, delivery_status FROM playergamehistory WHERE incidents > 385\"\n", "labels": {"reads": [{"table": "playergamehistory", "columns": ["incidents", "delivery_status"]}], "writes": [{"table": "az_drought_impact", "columns": ["incidents", "delivery_status"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"astronaut_medical_3\")\nsrc.write.insertInto(\"customer_transactions\", overwrite=True)\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": null}], "writes": [{"table": "customer_transactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO airport_aircraft SELECT site_name, peakhourid, is_organic FROM traditionalarts WHERE site_name > 270\"\n", "labels": {"reads": [{"table": "traditionalarts", "columns": ["site_name", "peakhourid", "is_organic"]}], "writes": [{"table": "airport_aircraft", "columns": ["site_name", "peakhourid", "is_organic"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT person_name, regulation FROM emergencies LIMIT 195\")\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ocean_depths SELECT menu_category, friend, project_education, attack_country FROM school WHERE menu_category > 45\")\n", "labels": {"reads": [{"table": "emergencies", "columns": ["person_name", "regulation"]}, {"table": "school", "columns": ["menu_category", "friend", "project_education", "attack_country"]}], "writes": [{"table": "ocean_depths", "columns": ["menu_category", "friend", "project_education", "attack_country"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ingredient_sourcing\")\nsrc.write.insertInto(\"ads.users_full\", overwrite=True)\n", "labels": {"reads": [{"table": "ingredient_sourcing", "columns": null}], "writes": [{"table": "ads.users_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table industrial_building_energy_efficiency --target-dir /tmp/land\n", "labels": {"reads": [{"table": "industrial_building_energy_efficiency", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tree_habitat_associations (sid, inclusive) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tree_habitat_associations", "columns": ["sid", "inclusive"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO haircare_sales SELECT bioprocess_name, technology, language FROM vesselarrivals WHERE bioprocess_name > 23\"], check=True)\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": ["bioprocess_name", "technology", "language"]}], "writes": [{"table": "haircare_sales", "columns": ["bioprocess_name", "technology", "language"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"customer_master_index\")\nsave_to_sink(df, \"companies\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": [{"table": "companies", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ethical_ai\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tryout\")\n", "labels": {"reads": [{"table": "ethical_ai", "columns": null}], "writes": [{"table": "tryout", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"domesticconferences\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tech_accessibility_funding\")\n", "labels": {"reads": [{"table": "domesticconferences", "columns": null}], "writes": [{"table": "tech_accessibility_funding", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT foreign, unit_id FROM artist\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"event_attendance\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "artist", "columns": ["foreign", "unit_id"]}], "writes": [{"table": "event_attendance", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT product_size, programoutcomeid FROM dwd.dwd_exposure_df\", engine)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"shop\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": ["product_size", "programoutcomeid"]}], "writes": [{"table": "shop", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT company_gender, sentence_id FROM attendance LIMIT 207\")\nimport logging\nspark.sql(\"INSERT INTO ai_ethics_policies SELECT injury, industry, equipment FROM neodymium_prices WHERE injury > 39\")\n", "labels": {"reads": [{"table": "attendance", "columns": ["company_gender", "sentence_id"]}, {"table": "neodymium_prices", "columns": ["injury", "industry", "equipment"]}], "writes": [{"table": "ai_ethics_policies", "columns": ["injury", "industry", "equipment"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO benefits_overpayments SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO freshwaterfinfish SELECT ai_model, strainname, tour_id FROM all_programs WHERE ai_model > 196\")\n", "labels": {"reads": [{"table": "all_programs", "columns": ["ai_model", "strainname", "tour_id"]}], "writes": [{"table": "freshwaterfinfish", "columns": ["ai_model", "strainname", "tour_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO digital_trends SELECT occupancy_rate, socially_responsible, team_id_br FROM threatintelligence WHERE occupancy_rate > 464\")\n", "labels": {"reads": [{"table": "threatintelligence", "columns": ["occupancy_rate", "socially_responsible", "team_id_br"]}], "writes": [{"table": "digital_trends", "columns": ["occupancy_rate", "socially_responsible", "team_id_br"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO fabricdata SELECT coach_id, family_name, case_type, neighborhood_id FROM labor_costs WHERE coach_id > 455\"\n", "labels": {"reads": [{"table": "labor_costs", "columns": ["coach_id", "family_name", "case_type", "neighborhood_id"]}], "writes": [{"table": "fabricdata", "columns": ["coach_id", "family_name", "case_type", "neighborhood_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.shipments_delta\").toPandas()\ndf[[\"unit_id\", \"annual_interchanges\"]].to_sql(\"asia_events\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": null}], "writes": [{"table": "asia_events", "columns": ["unit_id", "annual_interchanges"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"whale_sightings\")\nsink_to_store(df, \"eco_materials\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}], "writes": [{"table": "eco_materials", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO electricvehicleadoption SELECT planned_delivery_date, meal_name FROM urbanagricrop WHERE planned_delivery_date > 1\"\n", "labels": {"reads": [{"table": "urbanagricrop", "columns": ["planned_delivery_date", "meal_name"]}], "writes": [{"table": "electricvehicleadoption", "columns": ["planned_delivery_date", "meal_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_tourism_practices\").toPandas()\ndf[[\"bathroom_count\", \"phone\"]].to_sql(\"agro_regions\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": null}], "writes": [{"table": "agro_regions", "columns": ["bathroom_count", "phone"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artist\").toPandas()\ndf[[\"is_hybrid\", \"closure_authorised_by_staff_id\"]].to_sql(\"cmi_cross_references\", engine, index=False)\n", "labels": {"reads": [{"table": "artist", "columns": null}], "writes": [{"table": "cmi_cross_references", "columns": ["is_hybrid", "closure_authorised_by_staff_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO claims SELECT a.publication_id, b.student_id FROM party_forms a JOIN vehiclemodels b ON a.log_entry_date = b.log_entry_date\"\n", "labels": {"reads": [{"table": "party_forms", "columns": null}, {"table": "vehiclemodels", "columns": null}], "writes": [{"table": "claims", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws.exposure_df\", conn)\ndf.to_sql(\"student_courses\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws.exposure_df", "columns": null}], "writes": [{"table": "student_courses", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT online_dispute_resolution, retail_price FROM gameattendance LIMIT 252\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO furniture SELECT target_u_id, living_wage FROM operate_company WHERE target_u_id > 254\")\n", "labels": {"reads": [{"table": "gameattendance", "columns": ["online_dispute_resolution", "retail_price"]}, {"table": "operate_company", "columns": ["target_u_id", "living_wage"]}], "writes": [{"table": "furniture", "columns": ["target_u_id", "living_wage"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_products_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"manufacturers\")\n", "labels": {"reads": [{"table": "stg.stg_products_delta", "columns": null}], "writes": [{"table": "manufacturers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO threat_severity SELECT * FROM legacy\ncur.execute(\"SELECT centername, cargoid FROM courses LIMIT 270\")\n", "labels": {"reads": [{"table": "courses", "columns": ["centername", "cargoid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO functional_areas SELECT collection_id, staff_first_name, vehicle_flight_number, student FROM bikerental WHERE collection_id > 75\"\n", "labels": {"reads": [{"table": "bikerental", "columns": ["collection_id", "staff_first_name", "vehicle_flight_number", "student"]}], "writes": [{"table": "functional_areas", "columns": ["collection_id", "staff_first_name", "vehicle_flight_number", "student"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO demographics SELECT agreementid, blockcode, medium FROM trust WHERE agreementid > 51;\nEOF\n", "labels": {"reads": [{"table": "trust", "columns": ["agreementid", "blockcode", "medium"]}], "writes": [{"table": "demographics", "columns": ["agreementid", "blockcode", "medium"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dataset, state_code FROM courses\", engine)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"phishing_attempts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "courses", "columns": ["dataset", "state_code"]}], "writes": [{"table": "phishing_attempts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO higher_ed.publications SELECT impactid, route, materialtype FROM daily_revenue WHERE impactid > 162\"\n", "labels": {"reads": [{"table": "daily_revenue", "columns": ["impactid", "route", "materialtype"]}], "writes": [{"table": "higher_ed.publications", "columns": ["impactid", "route", "materialtype"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT bicycle_id, donationid FROM election LIMIT 2\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO cotton_source SELECT time_year, made_in_usa, award, bname FROM tournaments WHERE time_year > 234\")\n", "labels": {"reads": [{"table": "election", "columns": ["bicycle_id", "donationid"]}, {"table": "tournaments", "columns": ["time_year", "made_in_usa", "award", "bname"]}], "writes": [{"table": "cotton_source", "columns": ["time_year", "made_in_usa", "award", "bname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.events_delta SELECT waste_type, max_dissolved_oxygen, participatedinesports, day_number FROM footwear WHERE waste_type > 202\"\n", "labels": {"reads": [{"table": "footwear", "columns": ["waste_type", "max_dissolved_oxygen", "participatedinesports", "day_number"]}], "writes": [{"table": "bi.events_delta", "columns": ["waste_type", "max_dissolved_oxygen", "participatedinesports", "day_number"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT exit_type, donorname FROM public.ev_sales\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ndf.to_sql(\"voyages\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "public.ev_sales", "columns": ["exit_type", "donorname"]}], "writes": [{"table": "voyages", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT working_horses, installed_date FROM programs LIMIT 380\")\nimport logging\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ads_refunds_full SELECT storeid, unit, policyname FROM all_programs WHERE storeid > 299\")\n", "labels": {"reads": [{"table": "programs", "columns": ["working_horses", "installed_date"]}, {"table": "all_programs", "columns": ["storeid", "unit", "policyname"]}], "writes": [{"table": "ads_refunds_full", "columns": ["storeid", "unit", "policyname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"military_equipment\")\nsave_to_warehouse(df, \"immunizationrates\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "military_equipment", "columns": null}], "writes": [{"table": "immunizationrates", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"dw.clicks_di\")\nsave_to_store(df, \"circularsupplychain\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw.clicks_di", "columns": null}], "writes": [{"table": "circularsupplychain", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO roller_coaster (avg_usage, amount_claimed) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "roller_coaster", "columns": ["avg_usage", "amount_claimed"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT oct, wellbeing_score FROM habitats\", engine)\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"claims_documents\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "habitats", "columns": ["oct", "wellbeing_score"]}], "writes": [{"table": "claims_documents", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO water_usage SELECT completion_year, last_year, user_login, firstdonationdate FROM therapy_session WHERE completion_year > 123\"\n", "labels": {"reads": [{"table": "therapy_session", "columns": ["completion_year", "last_year", "user_login", "firstdonationdate"]}], "writes": [{"table": "water_usage", "columns": ["completion_year", "last_year", "user_login", "firstdonationdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"docking\")\nsrc.write.insertInto(\"gardens\", overwrite=True)\n", "labels": {"reads": [{"table": "docking", "columns": null}], "writes": [{"table": "gardens", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"routes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"biotech_startups\")\n", "labels": {"reads": [{"table": "routes", "columns": null}], "writes": [{"table": "biotech_startups", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM communitypolicing\", conn)\ndf.to_sql(\"program_history\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "communitypolicing", "columns": null}], "writes": [{"table": "program_history", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table agriculturalinvestments --columns is_vegan,location_text --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "agriculturalinvestments", "columns": ["is_vegan", "location_text"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.ods_users_di\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dorm\")\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}], "writes": [{"table": "dorm", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO dwd.dwd_products SELECT port_code, typical_selling_price FROM fan_purchases WHERE port_code > 100\"\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["port_code", "typical_selling_price"]}], "writes": [{"table": "dwd.dwd_products", "columns": ["port_code", "typical_selling_price"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT staff_last_name, complaint_date FROM geothermal_power_plants LIMIT 167\")\nmetrics.append(round(score, 4))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO public.developers SELECT product_category_description, steps, hourdate FROM sportsinfo WHERE product_category_description > 121\")\n", "labels": {"reads": [{"table": "geothermal_power_plants", "columns": ["staff_last_name", "complaint_date"]}, {"table": "sportsinfo", "columns": ["product_category_description", "steps", "hourdate"]}], "writes": [{"table": "public.developers", "columns": ["product_category_description", "steps", "hourdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart_cart_item_di SELECT savingsid, fleet_series, transaction_product, date_left_staff FROM provider_training WHERE savingsid > 178\"\n", "labels": {"reads": [{"table": "provider_training", "columns": ["savingsid", "fleet_series", "transaction_product", "date_left_staff"]}], "writes": [{"table": "mart_cart_item_di", "columns": ["savingsid", "fleet_series", "transaction_product", "date_left_staff"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO seal_population SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO satellitedata SELECT postal_code, country1, movieid FROM bi.device_log WHERE postal_code > 39\"\n", "labels": {"reads": [{"table": "bi.device_log", "columns": ["postal_code", "country1", "movieid"]}], "writes": [{"table": "satellitedata", "columns": ["postal_code", "country1", "movieid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO dwd.dwd_coupon_use_df SELECT resolution, port FROM workforce_development WHERE resolution > 329\"\n", "labels": {"reads": [{"table": "workforce_development", "columns": ["resolution", "port"]}], "writes": [{"table": "dwd.dwd_coupon_use_df", "columns": ["resolution", "port"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO african_union_countries SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO dws.dws_coupon_use_full SELECT a.emp_hiredate, b.framework_id FROM mexico_regions a JOIN game_scores b ON a.airport_name = b.airport_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mexico_regions", "columns": null}, {"table": "game_scores", "columns": null}], "writes": [{"table": "dws.dws_coupon_use_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO company_info SELECT a.star_rating_code, b.date_of_attendance FROM ads_cart_item_hourly a JOIN construction_union b ON a.race = b.race\"\n", "labels": {"reads": [{"table": "ads_cart_item_hourly", "columns": null}, {"table": "construction_union", "columns": null}], "writes": [{"table": "company_info", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fireincidents\"\n", "labels": {"reads": [{"table": "fireincidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"electric_vehicle_stats\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"airportdata\")\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": [{"table": "airportdata", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT department_id, org_size FROM user_workouts_march LIMIT 408\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": ["department_id", "org_size"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vulnerability_name, fault_log_entry_datetime FROM authors\", engine)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"drought_impact\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "authors", "columns": ["vulnerability_name", "fault_log_entry_datetime"]}], "writes": [{"table": "drought_impact", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT injured, production_usage FROM rebounds LIMIT 237;\nINSERT INTO soilmoisturedata SELECT primary_advisor, heart_rate, max_dissolved_oxygen, tech FROM total_consumption WHERE primary_advisor > 243;\nSQL\n", "labels": {"reads": [{"table": "rebounds", "columns": ["injured", "production_usage"]}, {"table": "total_consumption", "columns": ["primary_advisor", "heart_rate", "max_dissolved_oxygen", "tech"]}], "writes": [{"table": "soilmoisturedata", "columns": ["primary_advisor", "heart_rate", "max_dissolved_oxygen", "tech"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_in_location_from, company_gender FROM exhibition\", engine)\nimport logging\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"item\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "exhibition", "columns": ["date_in_location_from", "company_gender"]}], "writes": [{"table": "item", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dw.dw_inventory_delta SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM organization\", conn)\ndf.to_sql(\"defense_contractors\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "organization", "columns": null}], "writes": [{"table": "defense_contractors", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"athlete_stats\").toPandas()\ndf[[\"port_id\", \"follows_ethical_practices\"]].to_sql(\"market_share\", engine, index=False)\n", "labels": {"reads": [{"table": "athlete_stats", "columns": null}], "writes": [{"table": "market_share", "columns": ["port_id", "follows_ethical_practices"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table counties --columns person_id,graphics_mode --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "counties", "columns": ["person_id", "graphics_mode"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.coupon_use SELECT price, resource, nickname, points_per_game FROM rural.bus_trips WHERE price > 62\"], check=True)\n", "labels": {"reads": [{"table": "rural.bus_trips", "columns": ["price", "resource", "nickname", "points_per_game"]}], "writes": [{"table": "stg.coupon_use", "columns": ["price", "resource", "nickname", "points_per_game"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO housing_investments SELECT primary_advisor, show_name FROM student_tests_taken WHERE primary_advisor > 282\"\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": ["primary_advisor", "show_name"]}], "writes": [{"table": "housing_investments", "columns": ["primary_advisor", "show_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ads_sessions_di depends on crops_table\ndbt run --models ads_sessions_di --vars 'source: crops_table'\n", "labels": {"reads": [{"table": "crops_table", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO tracklists SELECT a.sighting_id, b.recruitername FROM mobile_plans a JOIN recycling_rates_state b ON a.genre_is = b.genre_is\"\n", "labels": {"reads": [{"table": "mobile_plans", "columns": null}, {"table": "recycling_rates_state", "columns": null}], "writes": [{"table": "tracklists", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.co2_amount > 158).all()\n# src table: ingredientsvegancrueltyfree\nengine.execute(\"INSERT INTO parties SELECT * FROM ingredientsvegancrueltyfree\")\n", "labels": {"reads": [{"table": "ingredientsvegancrueltyfree", "columns": null}], "writes": [{"table": "parties", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO labels SELECT song_id, date_of_latest_revision, policytype FROM art_workshops WHERE song_id > 275\"], check=True)\n", "labels": {"reads": [{"table": "art_workshops", "columns": ["song_id", "date_of_latest_revision", "policytype"]}], "writes": [{"table": "labels", "columns": ["song_id", "date_of_latest_revision", "policytype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT media_outlet, coach_id FROM issues\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"satellite_missions_large\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "issues", "columns": ["media_outlet", "coach_id"]}], "writes": [{"table": "satellite_missions_large", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ai_safety_papers2 (chargeable_amount, hotel_chain_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ai_safety_papers2", "columns": ["chargeable_amount", "hotel_chain_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO bi_orders_daily SELECT budget_allocation, acc_percent, station FROM dorm WHERE budget_allocation > 438\")\n", "labels": {"reads": [{"table": "dorm", "columns": ["budget_allocation", "acc_percent", "station"]}], "writes": [{"table": "bi_orders_daily", "columns": ["budget_allocation", "acc_percent", "station"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT trainingid, mining_operation_id FROM broadband_providers\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"global_tournament\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "broadband_providers", "columns": ["trainingid", "mining_operation_id"]}], "writes": [{"table": "global_tournament", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_payments_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"criminal_cases\")\n", "labels": {"reads": [{"table": "bi.bi_payments_delta", "columns": null}], "writes": [{"table": "criminal_cases", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"volume\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"apartment_bookings\")\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": [{"table": "apartment_bookings", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model policy_areas depends on mailshot_customers\ndbt build --models policy_areas --vars 'source: mailshot_customers'\n", "labels": {"reads": [{"table": "mailshot_customers", "columns": null}], "writes": [{"table": "policy_areas", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table satellitematerials --columns hotel_chain_id,policyholderid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "satellitematerials", "columns": ["hotel_chain_id", "policyholderid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"parties\")\nsrc.write.insertInto(\"field4_precip\", overwrite=True)\n", "labels": {"reads": [{"table": "parties", "columns": null}], "writes": [{"table": "field4_precip", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO coach SELECT a.investors, b.dapp_name FROM record a JOIN traffic_violations b ON a.player_id = b.player_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "record", "columns": null}, {"table": "traffic_violations", "columns": null}], "writes": [{"table": "coach", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM conservation_programs\", conn)\ndf.to_sql(\"dancefunding\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "conservation_programs", "columns": null}], "writes": [{"table": "dancefunding", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd_payments_delta --columns genrename,state_province_county --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd_payments_delta", "columns": ["genrename", "state_province_county"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT batting_average, adults FROM carbon_footprint LIMIT 333\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": ["batting_average", "adults"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tasks SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table candidates --target-dir /tmp/land\n", "labels": {"reads": [{"table": "candidates", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_show\").toPandas()\ndf[[\"booking_date\", \"caloric_content\"]].to_sql(\"ads.risk_score\", engine, index=False)\n", "labels": {"reads": [{"table": "auto_show", "columns": null}], "writes": [{"table": "ads.risk_score", "columns": ["booking_date", "caloric_content"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT total_spent, work_type FROM stg.campaigns_daily LIMIT 220;\nINSERT INTO opendatainitiatives SELECT openning_year, purchaseid, authors, employee_name FROM outcomes WHERE openning_year > 464;\nSQL\n", "labels": {"reads": [{"table": "stg.campaigns_daily", "columns": ["total_spent", "work_type"]}, {"table": "outcomes", "columns": ["openning_year", "purchaseid", "authors", "employee_name"]}], "writes": [{"table": "opendatainitiatives", "columns": ["openning_year", "purchaseid", "authors", "employee_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT genreid, document_structure_code FROM cybersecurity_strategies LIMIT 191;\nINSERT INTO community_education_programs SELECT wrestler_id, resident_id FROM indie_artists WHERE wrestler_id > 254;\nSQL\n", "labels": {"reads": [{"table": "cybersecurity_strategies", "columns": ["genreid", "document_structure_code"]}, {"table": "indie_artists", "columns": ["wrestler_id", "resident_id"]}], "writes": [{"table": "community_education_programs", "columns": ["wrestler_id", "resident_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO completed_training SELECT * FROM legacy\ncur.execute(\"SELECT project_name, ei_category FROM tree_species LIMIT 470\")\n", "labels": {"reads": [{"table": "tree_species", "columns": ["project_name", "ei_category"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artwork_styles\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"trips\")\n", "labels": {"reads": [{"table": "artwork_styles", "columns": null}], "writes": [{"table": "trips", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"safety_testing\")\ndump_to_sink(df, \"num_employees\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "safety_testing", "columns": null}], "writes": [{"table": "num_employees", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenance_contracts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"field4_precip\")\n", "labels": {"reads": [{"table": "maintenance_contracts", "columns": null}], "writes": [{"table": "field4_precip", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO biotech_startups SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"indigenous_communities\").toPandas()\ndf[[\"fundingagency\", \"movieid\"]].to_sql(\"veteran_occupations\", engine, index=False)\n", "labels": {"reads": [{"table": "indigenous_communities", "columns": null}], "writes": [{"table": "veteran_occupations", "columns": ["fundingagency", "movieid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"transport\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"customer\")\n", "labels": {"reads": [{"table": "transport", "columns": null}], "writes": [{"table": "customer", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table cerium_production --target-dir /tmp/land\n", "labels": {"reads": [{"table": "cerium_production", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table music --target-dir /tmp/land\n", "labels": {"reads": [{"table": "music", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO market SELECT completed_course, min_dew_point_f, programarea FROM customer_month WHERE completed_course > 487\"\n", "labels": {"reads": [{"table": "customer_month", "columns": ["completed_course", "min_dew_point_f", "programarea"]}], "writes": [{"table": "market", "columns": ["completed_course", "min_dew_point_f", "programarea"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shariah_compliant_finance depends on ai_papers\ndbt run -s shariah_compliant_finance --vars '{\"source_table\":\"ai_papers\"}'\n", "labels": {"reads": [{"table": "ai_papers", "columns": null}], "writes": [{"table": "shariah_compliant_finance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table communityevents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "communityevents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"producersnewmexico\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO channel SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO artist_info SELECT contract_end_date, participant_details FROM apac_hotel_views WHERE contract_end_date > 177\"\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["contract_end_date", "participant_details"]}], "writes": [{"table": "artist_info", "columns": ["contract_end_date", "participant_details"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO inspections SELECT personnelbranch, date_of_birth, violationtype, organic FROM consumer WHERE personnelbranch > 449;\nEOF\n", "labels": {"reads": [{"table": "consumer", "columns": ["personnelbranch", "date_of_birth", "violationtype", "organic"]}], "writes": [{"table": "inspections", "columns": ["personnelbranch", "date_of_birth", "violationtype", "organic"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO climate_adaptation_re SELECT clinic_id, calendar, took_office FROM labour_productivity WHERE clinic_id > 144;\nEOF\n", "labels": {"reads": [{"table": "labour_productivity", "columns": ["clinic_id", "calendar", "took_office"]}], "writes": [{"table": "climate_adaptation_re", "columns": ["clinic_id", "calendar", "took_office"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws_cart_item (ingredient_id, depth) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws_cart_item", "columns": ["ingredient_id", "depth"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO carbon_offsets SELECT programoutcomeid, culturalcompetency FROM ods.products_hourly WHERE programoutcomeid > 243;\nEOF\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": ["programoutcomeid", "culturalcompetency"]}], "writes": [{"table": "carbon_offsets", "columns": ["programoutcomeid", "culturalcompetency"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart_exposure_di SELECT a.permits_issued, b.text_of_notes FROM resilience_infrastructure a JOIN labels b ON a.protected = b.protected\"\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": null}, {"table": "labels", "columns": null}], "writes": [{"table": "mart_exposure_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vessel_positions\", conn)\ndf.to_sql(\"ocean_floor_mapping\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vessel_positions", "columns": null}], "writes": [{"table": "ocean_floor_mapping", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.coupon_use\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"solana_transactions\")\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": null}], "writes": [{"table": "solana_transactions", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO government_transparency SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.ads_inventory_df\", conn)\ndf.to_sql(\"ads_payments_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_inventory_df", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bank_info\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"airportdata\")\n", "labels": {"reads": [{"table": "bank_info", "columns": null}], "writes": [{"table": "airportdata", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO researchgrants SELECT max_dissolved_oxygen, staystart, injury FROM units WHERE max_dissolved_oxygen > 208\"\n", "labels": {"reads": [{"table": "units", "columns": ["max_dissolved_oxygen", "staystart", "injury"]}], "writes": [{"table": "researchgrants", "columns": ["max_dissolved_oxygen", "staystart", "injury"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO nutrition_facts SELECT a.games, b.max_aperture FROM furniture a JOIN military_bases b ON a.total_attendance = b.total_attendance\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "furniture", "columns": null}, {"table": "military_bases", "columns": null}], "writes": [{"table": "nutrition_facts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_vendors SELECT * FROM legacy\ncur.execute(\"SELECT invoice_id, review_text FROM exhibitiondetails LIMIT 18\")\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": ["invoice_id", "review_text"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cultural_competency_program\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cultural_competency_program", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads_refunds_full --columns stu_num,num_sustainable_materials --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads_refunds_full", "columns": ["stu_num", "num_sustainable_materials"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table incident_region --columns shippingmethod,eventdate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "incident_region", "columns": ["shippingmethod", "eventdate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO makeup_products (course_type, account_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "makeup_products", "columns": ["course_type", "account_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT customer_email_address, order_quantity FROM genetics.projects LIMIT 177\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO arctic_sightings SELECT veteran_unemployment_rate, driller FROM subway WHERE veteran_unemployment_rate > 136\")\n", "labels": {"reads": [{"table": "genetics.projects", "columns": ["customer_email_address", "order_quantity"]}, {"table": "subway", "columns": ["veteran_unemployment_rate", "driller"]}], "writes": [{"table": "arctic_sightings", "columns": ["veteran_unemployment_rate", "driller"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO port_visits SELECT date_and_date, appointment_duration, event_location FROM healthydelights WHERE date_and_date > 213\"\n", "labels": {"reads": [{"table": "healthydelights", "columns": ["date_and_date", "appointment_duration", "event_location"]}], "writes": [{"table": "port_visits", "columns": ["date_and_date", "appointment_duration", "event_location"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"grad_students\")\npush_to_target(df, \"vessel_positions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "grad_students", "columns": null}], "writes": [{"table": "vessel_positions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO productivity SELECT oct, has_spf, shares, school FROM india_ingredient_sourcing WHERE oct > 156\"\n", "labels": {"reads": [{"table": "india_ingredient_sourcing", "columns": ["oct", "has_spf", "shares", "school"]}], "writes": [{"table": "productivity", "columns": ["oct", "has_spf", "shares", "school"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table hotel_ratings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM playergamedata\"\n", "labels": {"reads": [{"table": "playergamedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT value, bname FROM australia_offset_programs LIMIT 146;\nINSERT INTO diversification_projects SELECT date_order_placed, main_industry, stu_hrs FROM ods_risk_score_delta WHERE date_order_placed > 39;\nSQL\n", "labels": {"reads": [{"table": "australia_offset_programs", "columns": ["value", "bname"]}, {"table": "ods_risk_score_delta", "columns": ["date_order_placed", "main_industry", "stu_hrs"]}], "writes": [{"table": "diversification_projects", "columns": ["date_order_placed", "main_industry", "stu_hrs"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO support_programs (service_type, fare_amount) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "support_programs", "columns": ["service_type", "fare_amount"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO immunization SELECT * FROM legacy\ncur.execute(\"SELECT rooms, working_year_starts FROM stg.member_point_df LIMIT 96\")\n", "labels": {"reads": [{"table": "stg.member_point_df", "columns": ["rooms", "working_year_starts"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"military_equipment_maintenance\")\nexport_to_warehouse(df, \"cloud_issues\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": null}], "writes": [{"table": "cloud_issues", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO coowners (country, cause) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "coowners", "columns": ["country", "cause"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO vehicle_registrations SELECT * FROM legacy\ncur.execute(\"SELECT date_opened, streams FROM ngo_funding LIMIT 237\")\n", "labels": {"reads": [{"table": "ngo_funding", "columns": ["date_opened", "streams"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"on_call\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"militarycyberops\")\n", "labels": {"reads": [{"table": "on_call", "columns": null}], "writes": [{"table": "militarycyberops", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.events_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT coownerid, attorney FROM bi.users_full LIMIT 412\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO customer_events SELECT temperature, oppose_rate, artifactid FROM leed_buildings WHERE temperature > 14\")\n", "labels": {"reads": [{"table": "bi.users_full", "columns": ["coownerid", "attorney"]}, {"table": "leed_buildings", "columns": ["temperature", "oppose_rate", "artifactid"]}], "writes": [{"table": "customer_events", "columns": ["temperature", "oppose_rate", "artifactid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_risk_score_df SELECT building_short_name, vol_id, silver FROM patients WHERE building_short_name > 101\"\n", "labels": {"reads": [{"table": "patients", "columns": ["building_short_name", "vol_id", "silver"]}], "writes": [{"table": "bi.bi_risk_score_df", "columns": ["building_short_name", "vol_id", "silver"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO justice_schemas.legal_tech_providers SELECT no_of_customers, trend_id, don_id FROM district_schools WHERE no_of_customers > 479\"\n", "labels": {"reads": [{"table": "district_schools", "columns": ["no_of_customers", "trend_id", "don_id"]}], "writes": [{"table": "justice_schemas.legal_tech_providers", "columns": ["no_of_customers", "trend_id", "don_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table italy_culture --target-dir /tmp/land\n", "labels": {"reads": [{"table": "italy_culture", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO marketing_budgets SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO government.region SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table market --columns individual_last_name,socially_responsible --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "market", "columns": ["individual_last_name", "socially_responsible"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.job_title > 248).all()\n# src table: socially_responsible_loans\nengine.execute(\"INSERT INTO communityengagementmetrics SELECT * FROM socially_responsible_loans\")\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": null}], "writes": [{"table": "communityengagementmetrics", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dw.dw_payments_full SELECT mine_location, customer_status_code, volunteer_quarter FROM products_in_events WHERE mine_location > 260\"\n", "labels": {"reads": [{"table": "products_in_events", "columns": ["mine_location", "customer_status_code", "volunteer_quarter"]}], "writes": [{"table": "dw.dw_payments_full", "columns": ["mine_location", "customer_status_code", "volunteer_quarter"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM wastedata\", conn)\ndf.to_sql(\"cybersecurity_vulnerabilities\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "wastedata", "columns": null}], "writes": [{"table": "cybersecurity_vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_acidity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"features\")\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": null}], "writes": [{"table": "features", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT common_name, phone_number FROM sales LIMIT 342\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO wellbeing_program_participants SELECT born_state, testdate FROM programs WHERE born_state > 478\")\n", "labels": {"reads": [{"table": "sales", "columns": ["common_name", "phone_number"]}, {"table": "programs", "columns": ["born_state", "testdate"]}], "writes": [{"table": "wellbeing_program_participants", "columns": ["born_state", "testdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dribbling, implementation_year FROM mart.mart_device_log_delta LIMIT 422\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "mart.mart_device_log_delta", "columns": ["dribbling", "implementation_year"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM wastewatertreatment\"\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO virtual_tours_oceania SELECT incidenttype, mailing_date, facility_name FROM recall_reports WHERE incidenttype > 251\"\n", "labels": {"reads": [{"table": "recall_reports", "columns": ["incidenttype", "mailing_date", "facility_name"]}], "writes": [{"table": "virtual_tours_oceania", "columns": ["incidenttype", "mailing_date", "facility_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM home_game\", conn)\ndf.to_sql(\"chemical_production_5\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "home_game", "columns": null}], "writes": [{"table": "chemical_production_5", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cosmetics.lipstick_spf_data\").toPandas()\ndf[[\"mode\", \"doctor_id\"]].to_sql(\"mealtypes\", engine, index=False)\n", "labels": {"reads": [{"table": "cosmetics.lipstick_spf_data", "columns": null}], "writes": [{"table": "mealtypes", "columns": ["mode", "doctor_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO transport SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT campaign_name, host_country FROM region_stats LIMIT 375\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "region_stats", "columns": ["campaign_name", "host_country"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tree_species\").toPandas()\ndf[[\"grantid\", \"undergraduate\"]].to_sql(\"schools\", engine, index=False)\n", "labels": {"reads": [{"table": "tree_species", "columns": null}], "writes": [{"table": "schools", "columns": ["grantid", "undergraduate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"excavationsites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marine_species_status\")\n", "labels": {"reads": [{"table": "excavationsites", "columns": null}], "writes": [{"table": "marine_species_status", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"usdaviolations\").toPandas()\ndf[[\"practiceid\", \"personnelbranch\"]].to_sql(\"ods.ods_campaigns_df\", engine, index=False)\n", "labels": {"reads": [{"table": "usdaviolations", "columns": null}], "writes": [{"table": "ods.ods_campaigns_df", "columns": ["practiceid", "personnelbranch"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO smartcitycosts SELECT graduate, membergender, lot_id, orderdate FROM dw.dw_sessions_delta WHERE graduate > 188\"\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": ["graduate", "membergender", "lot_id", "orderdate"]}], "writes": [{"table": "smartcitycosts", "columns": ["graduate", "membergender", "lot_id", "orderdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO arcticwildlifereserve (article_id, participant_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "arcticwildlifereserve", "columns": ["article_id", "participant_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO policy SELECT rehab_date, amount_due, injury FROM weekly_weather WHERE rehab_date > 295\"], check=True)\n", "labels": {"reads": [{"table": "weekly_weather", "columns": ["rehab_date", "amount_due", "injury"]}], "writes": [{"table": "policy", "columns": ["rehab_date", "amount_due", "injury"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO co2_emissions (premise_id, business_size) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "co2_emissions", "columns": ["premise_id", "business_size"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"researchprojects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "researchprojects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ethics_violations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"makeup_sales\")\n", "labels": {"reads": [{"table": "ethics_violations", "columns": null}], "writes": [{"table": "makeup_sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO mart.mart_payments_hourly SELECT a.record_id, b.attribute_id FROM trenches a JOIN dwd.dwd_events_delta b ON a.spf_level = b.spf_level\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trenches", "columns": null}, {"table": "dwd.dwd_events_delta", "columns": null}], "writes": [{"table": "mart.mart_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO urban_agriculture_initiatives SELECT sustainablepractices, production_cost FROM social_impact_bonds WHERE sustainablepractices > 274\")\n", "labels": {"reads": [{"table": "social_impact_bonds", "columns": ["sustainablepractices", "production_cost"]}], "writes": [{"table": "urban_agriculture_initiatives", "columns": ["sustainablepractices", "production_cost"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table miningoperations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "miningoperations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sponsorship_donations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"permit\")\n", "labels": {"reads": [{"table": "sponsorship_donations", "columns": null}], "writes": [{"table": "permit", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ingredientsvegancrueltyfree\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ingredientsvegancrueltyfree", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"item\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"trainingprograms\")\n", "labels": {"reads": [{"table": "item", "columns": null}], "writes": [{"table": "trainingprograms", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO species_forests SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hardware_model_name, room_count FROM community.donations LIMIT 453;\nINSERT INTO virtual_tours_oceania SELECT archaeologist_name, max_gust_speed_mph, fare FROM tree_types WHERE archaeologist_name > 149;\nSQL\n", "labels": {"reads": [{"table": "community.donations", "columns": ["hardware_model_name", "room_count"]}, {"table": "tree_types", "columns": ["archaeologist_name", "max_gust_speed_mph", "fare"]}], "writes": [{"table": "virtual_tours_oceania", "columns": ["archaeologist_name", "max_gust_speed_mph", "fare"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT grape, recorded_by_staff_id FROM temperature LIMIT 184\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "temperature", "columns": ["grape", "recorded_by_staff_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO high_risk SELECT * FROM legacy\ncur.execute(\"SELECT effort, is_sustainable FROM activities LIMIT 426\")\n", "labels": {"reads": [{"table": "activities", "columns": ["effort", "is_sustainable"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model projects depends on investor_activities\ndbt run --models projects --vars '{\"source_table\":\"investor_activities\"}'\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": [{"table": "projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT culture, farm_id FROM precipitation_data LIMIT 255;\nINSERT INTO winter_olympics SELECT salary, omim FROM defense_contracts WHERE salary > 430;\nSQL\n", "labels": {"reads": [{"table": "precipitation_data", "columns": ["culture", "farm_id"]}, {"table": "defense_contracts", "columns": ["salary", "omim"]}], "writes": [{"table": "winter_olympics", "columns": ["salary", "omim"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO mart.mart_sessions_di SELECT menuitemname, billid, building, virtual_tour_engagement_time FROM music_streaming WHERE menuitemname > 384\"\n", "labels": {"reads": [{"table": "music_streaming", "columns": ["menuitemname", "billid", "building", "virtual_tour_engagement_time"]}], "writes": [{"table": "mart.mart_sessions_di", "columns": ["menuitemname", "billid", "building", "virtual_tour_engagement_time"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT people_id, date_moved_in FROM iron_ore_production LIMIT 457\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["people_id", "date_moved_in"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table drought_data --columns date_of_ceremony,male_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "drought_data", "columns": ["date_of_ceremony", "male_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.device_log_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM humanitarian_aid\"\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO mental_health_professionals_2 SELECT amount_used, process_id, city_population, seasons FROM stg.stg_users_full WHERE amount_used > 104\")\n", "labels": {"reads": [{"table": "stg.stg_users_full", "columns": ["amount_used", "process_id", "city_population", "seasons"]}], "writes": [{"table": "mental_health_professionals_2", "columns": ["amount_used", "process_id", "city_population", "seasons"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table ods_risk_score_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.ods_member_point_df SELECT followers, annual_revenue, unitprice, eventname FROM state_usage WHERE followers > 477;\nEOF\n", "labels": {"reads": [{"table": "state_usage", "columns": ["followers", "annual_revenue", "unitprice", "eventname"]}], "writes": [{"table": "ods.ods_member_point_df", "columns": ["followers", "annual_revenue", "unitprice", "eventname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM product_reviews\"\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO org_climate_finance (shipped_to, suppliername) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "org_climate_finance", "columns": ["shipped_to", "suppliername"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mediterranean_salinity depends on demographics\ndbt build --select mediterranean_salinity --vars '{\"source_table\":\"demographics\"}'\n", "labels": {"reads": [{"table": "demographics", "columns": null}], "writes": [{"table": "mediterranean_salinity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO landfillcapacitybycountry SELECT num_of_factories, invoice_details, publication_year, workout_date FROM cosmetic_formula WHERE num_of_factories > 442\"\n", "labels": {"reads": [{"table": "cosmetic_formula", "columns": ["num_of_factories", "invoice_details", "publication_year", "workout_date"]}], "writes": [{"table": "landfillcapacitybycountry", "columns": ["num_of_factories", "invoice_details", "publication_year", "workout_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO traffic SELECT statename, yearadded, primary, home_city FROM wins WHERE statename > 76\"\n", "labels": {"reads": [{"table": "wins", "columns": ["statename", "yearadded", "primary", "home_city"]}], "writes": [{"table": "traffic", "columns": ["statename", "yearadded", "primary", "home_city"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.stg_users_full SELECT emp_num, emp_lname, attendance_date FROM party_forms WHERE emp_num > 385\"\n", "labels": {"reads": [{"table": "party_forms", "columns": ["emp_num", "emp_lname", "attendance_date"]}], "writes": [{"table": "stg.stg_users_full", "columns": ["emp_num", "emp_lname", "attendance_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workout\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chip_model\")\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": [{"table": "chip_model", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO concert_sales SELECT trainingname, price_in_dollars, contract_count FROM cb_agreements WHERE trainingname > 362\")\n", "labels": {"reads": [{"table": "cb_agreements", "columns": ["trainingname", "price_in_dollars", "contract_count"]}], "writes": [{"table": "concert_sales", "columns": ["trainingname", "price_in_dollars", "contract_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"infra_diversification\")\nsrc.write.insertInto(\"mart.mart_vendors\", overwrite=True)\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}], "writes": [{"table": "mart.mart_vendors", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT classid, menu_type FROM comments LIMIT 258\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "comments", "columns": ["classid", "menu_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"machine\").toPandas()\ndf[[\"status_code\", \"pixels\"]].to_sql(\"catalogs\", engine, index=False)\n", "labels": {"reads": [{"table": "machine", "columns": null}], "writes": [{"table": "catalogs", "columns": ["status_code", "pixels"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ai_safety (product_color, personnelbranch) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ai_safety", "columns": ["product_color", "personnelbranch"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO billstatus SELECT screening_id, time_second FROM fare_collection WHERE screening_id > 41\"], check=True)\n", "labels": {"reads": [{"table": "fare_collection", "columns": ["screening_id", "time_second"]}], "writes": [{"table": "billstatus", "columns": ["screening_id", "time_second"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO station SELECT jul, payment_type_code, tasktype, plant FROM aircraftsquadrons WHERE jul > 84\"\n", "labels": {"reads": [{"table": "aircraftsquadrons", "columns": ["jul", "payment_type_code", "tasktype", "plant"]}], "writes": [{"table": "station", "columns": ["jul", "payment_type_code", "tasktype", "plant"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model fertilizer depends on co2emissions\ndbt run -s fertilizer --vars 'source: co2emissions'\n", "labels": {"reads": [{"table": "co2emissions", "columns": null}], "writes": [{"table": "fertilizer", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO attribute_definitions SELECT * FROM legacy\ncur.execute(\"SELECT artwork_id, satellite FROM complaints LIMIT 376\")\n", "labels": {"reads": [{"table": "complaints", "columns": ["artwork_id", "satellite"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO regulatory_compliance SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT is_operational, metric FROM disaster_zones\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"vulnerabilities\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "disaster_zones", "columns": ["is_operational", "metric"]}], "writes": [{"table": "vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO contracts SELECT value_points, donor_category, habitat_name, volunteer_quarter FROM trade_history WHERE value_points > 260\"\n", "labels": {"reads": [{"table": "trade_history", "columns": ["value_points", "donor_category", "habitat_name", "volunteer_quarter"]}], "writes": [{"table": "contracts", "columns": ["value_points", "donor_category", "habitat_name", "volunteer_quarter"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"colorado_river_basin\")\ndump_to_store(df, \"weather_record\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": null}], "writes": [{"table": "weather_record", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO athletes SELECT posted_at, last_name, medical_condition FROM race WHERE posted_at > 9\"\n", "labels": {"reads": [{"table": "race", "columns": ["posted_at", "last_name", "medical_condition"]}], "writes": [{"table": "athletes", "columns": ["posted_at", "last_name", "medical_condition"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO textile_suppliers SELECT planned_delivery_date, investor_id FROM bi_inventory_hourly WHERE planned_delivery_date > 475\"\n", "labels": {"reads": [{"table": "bi_inventory_hourly", "columns": ["planned_delivery_date", "investor_id"]}], "writes": [{"table": "textile_suppliers", "columns": ["planned_delivery_date", "investor_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dispensary_sales (venue_id, neighborhood) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dispensary_sales", "columns": ["venue_id", "neighborhood"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO engineer_skills SELECT industry_4_0, delivery_id, handling_id FROM airlines WHERE industry_4_0 > 12\"], check=True)\n", "labels": {"reads": [{"table": "airlines", "columns": ["industry_4_0", "delivery_id", "handling_id"]}], "writes": [{"table": "engineer_skills", "columns": ["industry_4_0", "delivery_id", "handling_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_events_hourly SELECT a.functional_area_description, b.sd_id FROM noise_pollution a JOIN marine_life_research_stations b ON a.team_id_winner = b.team_id_winner\"\n", "labels": {"reads": [{"table": "noise_pollution", "columns": null}, {"table": "marine_life_research_stations", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO staff_roles SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model wind_projects depends on state_water_usage\ndbt run --select wind_projects --vars '{\"source_table\":\"state_water_usage\"}'\n", "labels": {"reads": [{"table": "state_water_usage", "columns": null}], "writes": [{"table": "wind_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO cosmetics (vendor_state, participatedinesports) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "cosmetics", "columns": ["vendor_state", "participatedinesports"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model innovation_grants depends on geologicalsurvey\ndbt build -s innovation_grants --vars '{\"src\":\"geologicalsurvey\"}'\n", "labels": {"reads": [{"table": "geologicalsurvey", "columns": null}], "writes": [{"table": "innovation_grants", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO programoutcomes SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO state_contracts SELECT a.shares, b.mid FROM bi.bi_risk_score_df a JOIN purchase b ON a.dec = b.dec\"\n", "labels": {"reads": [{"table": "bi.bi_risk_score_df", "columns": null}, {"table": "purchase", "columns": null}], "writes": [{"table": "state_contracts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model electric_vehicle_stats depends on stock\ndbt run -s electric_vehicle_stats --vars 'source: stock'\n", "labels": {"reads": [{"table": "stock", "columns": null}], "writes": [{"table": "electric_vehicle_stats", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"baseball_teams\")\nsrc.write.insertInto(\"government_funding\", overwrite=True)\n", "labels": {"reads": [{"table": "baseball_teams", "columns": null}], "writes": [{"table": "government_funding", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT card_type_code, allocation_type FROM login_attempts\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ndf.to_sql(\"funding_rounds\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["card_type_code", "allocation_type"]}], "writes": [{"table": "funding_rounds", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO malicious_activity SELECT profession, gradepoint, year_join, total_beds FROM sustainableproduction WHERE profession > 358\")\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": ["profession", "gradepoint", "year_join", "total_beds"]}], "writes": [{"table": "malicious_activity", "columns": ["profession", "gradepoint", "year_join", "total_beds"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO smartcities (port_code, destination_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "smartcities", "columns": ["port_code", "destination_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_orders_full\").toPandas()\ndf[[\"transit_passengers\", \"staff_address_id\"]].to_sql(\"labor_statistics\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_orders_full", "columns": null}], "writes": [{"table": "labor_statistics", "columns": ["transit_passengers", "staff_address_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table faculty --columns inspection_time,evaluationid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "faculty", "columns": ["inspection_time", "evaluationid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO fairtradecertification SELECT gas_production_2020, missionid FROM advisor WHERE gas_production_2020 > 70;\nEOF\n", "labels": {"reads": [{"table": "advisor", "columns": ["gas_production_2020", "missionid"]}], "writes": [{"table": "fairtradecertification", "columns": ["gas_production_2020", "missionid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO military_bases SELECT region_name, spill_name FROM supply_chain WHERE region_name > 88\"\n", "labels": {"reads": [{"table": "supply_chain", "columns": ["region_name", "spill_name"]}], "writes": [{"table": "military_bases", "columns": ["region_name", "spill_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"healthcare_access_v2\")\ndump_to_output(df, \"player_stats\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": null}], "writes": [{"table": "player_stats", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM section\", conn)\ndf.to_sql(\"traffic\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "section", "columns": null}], "writes": [{"table": "traffic", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO zipcodes SELECT census_ranking, amountdonated, max_dew_point_f, town_city FROM unionnegotiations WHERE census_ranking > 104\")\n", "labels": {"reads": [{"table": "unionnegotiations", "columns": ["census_ranking", "amountdonated", "max_dew_point_f", "town_city"]}], "writes": [{"table": "zipcodes", "columns": ["census_ranking", "amountdonated", "max_dew_point_f", "town_city"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM visits\"\n", "labels": {"reads": [{"table": "visits", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.shipments_df SELECT animal_type, virtual_tour_engagement_time, name_last, policy FROM cotton_source WHERE animal_type > 464\"], check=True)\n", "labels": {"reads": [{"table": "cotton_source", "columns": ["animal_type", "virtual_tour_engagement_time", "name_last", "policy"]}], "writes": [{"table": "mart.shipments_df", "columns": ["animal_type", "virtual_tour_engagement_time", "name_last", "policy"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"part_faults\")\nsrc.write.insertInto(\"ecohousing\", overwrite=True)\n", "labels": {"reads": [{"table": "part_faults", "columns": null}], "writes": [{"table": "ecohousing", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT deliveryaddress, mealname FROM people LIMIT 433\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "people", "columns": ["deliveryaddress", "mealname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.device_log_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shariah_compliant_finance\")\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": null}], "writes": [{"table": "shariah_compliant_finance", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO department_store_chain SELECT a.operation_type, b.international_passengers FROM soil_moisture a JOIN ai_safety_papers2 b ON a.store_name = b.store_name\"\n", "labels": {"reads": [{"table": "soil_moisture", "columns": null}, {"table": "ai_safety_papers2", "columns": null}], "writes": [{"table": "department_store_chain", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"daily_oil_production\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"exit_strategy\")\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.bi_shipments SELECT market_value, focal_length_mm FROM fashion_trend_data WHERE market_value > 302\"\n", "labels": {"reads": [{"table": "fashion_trend_data", "columns": ["market_value", "focal_length_mm"]}], "writes": [{"table": "bi.bi_shipments", "columns": ["market_value", "focal_length_mm"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ytterbium_supply (recipient_id, therapy_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ytterbium_supply", "columns": ["recipient_id", "therapy_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dw.dw_sessions_delta SELECT implementation_date, patient_age, visit_id, restaurant_name FROM students WHERE implementation_date > 386\"\n", "labels": {"reads": [{"table": "students", "columns": ["implementation_date", "patient_age", "visit_id", "restaurant_name"]}], "writes": [{"table": "dw.dw_sessions_delta", "columns": ["implementation_date", "patient_age", "visit_id", "restaurant_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO crime_incidents SELECT a.classtype, b.sale_volume FROM county a JOIN platformg b ON a.sentence_length = b.sentence_length\"\n", "labels": {"reads": [{"table": "county", "columns": null}, {"table": "platformg", "columns": null}], "writes": [{"table": "crime_incidents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO video_content SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT chw_id, allergy FROM initiatives_3 LIMIT 378\")\nimport logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO user_activity SELECT dance_form, pd_id, pricepergram FROM government_transparency WHERE dance_form > 179\")\n", "labels": {"reads": [{"table": "initiatives_3", "columns": ["chw_id", "allergy"]}, {"table": "government_transparency", "columns": ["dance_form", "pd_id", "pricepergram"]}], "writes": [{"table": "user_activity", "columns": ["dance_form", "pd_id", "pricepergram"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO exhibition_visitors SELECT process_id, problem_description, country, caloric_content FROM protein WHERE process_id > 422\"\n", "labels": {"reads": [{"table": "protein", "columns": ["process_id", "problem_description", "country", "caloric_content"]}], "writes": [{"table": "exhibition_visitors", "columns": ["process_id", "problem_description", "country", "caloric_content"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO species_observations SELECT home_team_score, disability, stu_lname FROM event_attendance WHERE home_team_score > 149\"\n", "labels": {"reads": [{"table": "event_attendance", "columns": ["home_team_score", "disability", "stu_lname"]}], "writes": [{"table": "species_observations", "columns": ["home_team_score", "disability", "stu_lname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO union_membership SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.campaigns_daily\")\nsrc.write.insertInto(\"product_revenue\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.campaigns_daily", "columns": null}], "writes": [{"table": "product_revenue", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO noise_pollution SELECT lanes, cost_id, good_or_bad_customer FROM fruitimport WHERE lanes > 18\")\n", "labels": {"reads": [{"table": "fruitimport", "columns": ["lanes", "cost_id", "good_or_bad_customer"]}], "writes": [{"table": "noise_pollution", "columns": ["lanes", "cost_id", "good_or_bad_customer"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"leed_buildings\").toPandas()\ndf[[\"fund_name\", \"lipstick_id\"]].to_sql(\"online_travel_agency\", engine, index=False)\n", "labels": {"reads": [{"table": "leed_buildings", "columns": null}], "writes": [{"table": "online_travel_agency", "columns": ["fund_name", "lipstick_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO livestock (friend, driller) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "livestock", "columns": ["friend", "driller"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.daily_co2_emission > 319).all()\n# src table: ethics_violations\nengine.execute(\"INSERT INTO preferences SELECT * FROM ethics_violations\")\n", "labels": {"reads": [{"table": "ethics_violations", "columns": null}], "writes": [{"table": "preferences", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO grant SELECT a.commodity, b.winning_pilot FROM student_access a JOIN stg.campaigns_daily b ON a.attribute_name = b.attribute_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "student_access", "columns": null}, {"table": "stg.campaigns_daily", "columns": null}], "writes": [{"table": "grant", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marine_species_indian SELECT a.technique, b.funding_amount FROM supportprograms a JOIN stg.stg_inventory_full b ON a.haslegalprecedent = b.haslegalprecedent\"\n", "labels": {"reads": [{"table": "supportprograms", "columns": null}, {"table": "stg.stg_inventory_full", "columns": null}], "writes": [{"table": "marine_species_indian", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_exposure_daily SELECT claimtype, amount_piad, donationyear, personal_name FROM cargos WHERE claimtype > 99\"\n", "labels": {"reads": [{"table": "cargos", "columns": ["claimtype", "amount_piad", "donationyear", "personal_name"]}], "writes": [{"table": "ads.ads_exposure_daily", "columns": ["claimtype", "amount_piad", "donationyear", "personal_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO healthbudget SELECT a.visit_month, b.decoration_theme FROM fairtradefactories a JOIN facility_production b ON a.f_id = b.f_id\"\n", "labels": {"reads": [{"table": "fairtradefactories", "columns": null}, {"table": "facility_production", "columns": null}], "writes": [{"table": "healthbudget", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT moisture, created_date FROM claims_processing_stages\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"hospital_equipment\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "claims_processing_stages", "columns": ["moisture", "created_date"]}], "writes": [{"table": "hospital_equipment", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_users_di SELECT attribute_value, customer_country FROM jupiter_missions WHERE attribute_value > 116\"\n", "labels": {"reads": [{"table": "jupiter_missions", "columns": ["attribute_value", "customer_country"]}], "writes": [{"table": "ods.ods_users_di", "columns": ["attribute_value", "customer_country"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO domesticconferences SELECT regionname, cityid, sale_year FROM innovation_grants WHERE regionname > 133\"\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["regionname", "cityid", "sale_year"]}], "writes": [{"table": "domesticconferences", "columns": ["regionname", "cityid", "sale_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO disease_prevalence SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"claims_processing_stages\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marine_species_status\")\n", "labels": {"reads": [{"table": "claims_processing_stages", "columns": null}], "writes": [{"table": "marine_species_status", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO team_franchise SELECT team_id_loser, initiativeid FROM dwd.products_hourly WHERE team_id_loser > 93\")\n", "labels": {"reads": [{"table": "dwd.products_hourly", "columns": ["team_id_loser", "initiativeid"]}], "writes": [{"table": "team_franchise", "columns": ["team_id_loser", "initiativeid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vehicles SELECT case_status, stu_hrs, num_developments, number_of_vessels FROM water_conservation WHERE case_status > 409\"\n", "labels": {"reads": [{"table": "water_conservation", "columns": ["case_status", "stu_hrs", "num_developments", "number_of_vessels"]}], "writes": [{"table": "vehicles", "columns": ["case_status", "stu_hrs", "num_developments", "number_of_vessels"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO nasa_mars_program SELECT order_details, date_of_ceremony, mental_health_status FROM vessel_safety WHERE order_details > 170\")\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["order_details", "date_of_ceremony", "mental_health_status"]}], "writes": [{"table": "nasa_mars_program", "columns": ["order_details", "date_of_ceremony", "mental_health_status"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.bi_orders_hourly SELECT employees, sid, productiondate, savingsid FROM constructors WHERE employees > 499\"\n", "labels": {"reads": [{"table": "constructors", "columns": ["employees", "sid", "productiondate", "savingsid"]}], "writes": [{"table": "bi.bi_orders_hourly", "columns": ["employees", "sid", "productiondate", "savingsid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM wrestler\", conn)\ndf.to_sql(\"labor_unions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "wrestler", "columns": null}], "writes": [{"table": "labor_unions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO infrastructure_projects (artist_gender, caseid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "infrastructure_projects", "columns": ["artist_gender", "caseid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO available_policies SELECT missing_data, filingdate FROM sustainability_metrics WHERE missing_data > 30\")\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": ["missing_data", "filingdate"]}], "writes": [{"table": "available_policies", "columns": ["missing_data", "filingdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO esa_missions SELECT wind_speed_mph, launch, launch_date, number_of_sightings FROM reporters WHERE wind_speed_mph > 343\"], check=True)\n", "labels": {"reads": [{"table": "reporters", "columns": ["wind_speed_mph", "launch", "launch_date", "number_of_sightings"]}], "writes": [{"table": "esa_missions", "columns": ["wind_speed_mph", "launch", "launch_date", "number_of_sightings"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mediators\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bank_info\")\n", "labels": {"reads": [{"table": "mediators", "columns": null}], "writes": [{"table": "bank_info", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT prominence, total_value_purchased FROM performances\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"atlantic_ocean\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "performances", "columns": ["prominence", "total_value_purchased"]}], "writes": [{"table": "atlantic_ocean", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO military_personnel_africa SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ll_hours, detected_at FROM musicgenre\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"bioprocess.engineering_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "musicgenre", "columns": ["ll_hours", "detected_at"]}], "writes": [{"table": "bioprocess.engineering_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.initiativename > 293).all()\n# src table: agency_satellites\nengine.execute(\"INSERT INTO impact_investments SELECT * FROM agency_satellites\")\n", "labels": {"reads": [{"table": "agency_satellites", "columns": null}], "writes": [{"table": "impact_investments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"arctic_sightings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"average\")\n", "labels": {"reads": [{"table": "arctic_sightings", "columns": null}], "writes": [{"table": "average", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"record\")\ndump_to_warehouse(df, \"ods.ods_member_point_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "record", "columns": null}], "writes": [{"table": "ods.ods_member_point_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO public.police_calls SELECT acidification_level, regionid FROM licenses WHERE acidification_level > 270\")\n", "labels": {"reads": [{"table": "licenses", "columns": ["acidification_level", "regionid"]}], "writes": [{"table": "public.police_calls", "columns": ["acidification_level", "regionid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dwd.coupon_use_full SELECT mission, service_type_description FROM vocals WHERE mission > 448;\nEOF\n", "labels": {"reads": [{"table": "vocals", "columns": ["mission", "service_type_description"]}], "writes": [{"table": "dwd.coupon_use_full", "columns": ["mission", "service_type_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO member_attendance SELECT cityid, target_name, supplier, cost_id FROM first_notification_of_loss WHERE cityid > 452\")\n", "labels": {"reads": [{"table": "first_notification_of_loss", "columns": ["cityid", "target_name", "supplier", "cost_id"]}], "writes": [{"table": "member_attendance", "columns": ["cityid", "target_name", "supplier", "cost_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT round_number, personnel FROM engineer_skills\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"ads.campaigns_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["round_number", "personnel"]}], "writes": [{"table": "ads.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dws.exposure SELECT a.yield_id, b.attendee_id FROM stg.refunds_daily a JOIN veterans b ON a.guest_id = b.guest_id\"\n", "labels": {"reads": [{"table": "stg.refunds_daily", "columns": null}, {"table": "veterans", "columns": null}], "writes": [{"table": "dws.exposure", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"permian_basin\")\nsrc.write.insertInto(\"cosmetic_formula\", overwrite=True)\n", "labels": {"reads": [{"table": "permian_basin", "columns": null}], "writes": [{"table": "cosmetic_formula", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model exposure_df depends on device_accessibility\ndbt run --select exposure_df --vars '{\"src\":\"device_accessibility\"}'\n", "labels": {"reads": [{"table": "device_accessibility", "columns": null}], "writes": [{"table": "exposure_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table education --columns facility_code,pass_fail --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "education", "columns": ["facility_code", "pass_fail"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO store_product SELECT round_amount, mean_sea_level_pressure_inches, spf_level FROM dws.cart_item_full WHERE round_amount > 60\"\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": ["round_amount", "mean_sea_level_pressure_inches", "spf_level"]}], "writes": [{"table": "store_product", "columns": ["round_amount", "mean_sea_level_pressure_inches", "spf_level"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO space_programs SELECT a.home_team_points, b.truck_details FROM surveylocations a JOIN autonomous_research b ON a.vulnerability_name = b.vulnerability_name\"\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}, {"table": "autonomous_research", "columns": null}], "writes": [{"table": "space_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_employees > 69).all()\n# src table: cybersecurityincidents\nengine.execute(\"INSERT INTO wrestler SELECT * FROM cybersecurityincidents\")\n", "labels": {"reads": [{"table": "cybersecurityincidents", "columns": null}], "writes": [{"table": "wrestler", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO miningoperations SELECT a.funding, b.machine_series FROM fabric a JOIN foodaid b ON a.plantid = b.plantid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fabric", "columns": null}, {"table": "foodaid", "columns": null}], "writes": [{"table": "miningoperations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.subject > 386).all()\n# src table: prison\nengine.execute(\"INSERT INTO mart.shipments_full SELECT * FROM prison\")\n", "labels": {"reads": [{"table": "prison", "columns": null}], "writes": [{"table": "mart.shipments_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM casesbyyear\"\n", "labels": {"reads": [{"table": "casesbyyear", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hospitallocations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.cart_item_di\")\n", "labels": {"reads": [{"table": "hospitallocations", "columns": null}], "writes": [{"table": "dws.cart_item_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"garmentproduction\")\nsrc.write.insertInto(\"cities\", overwrite=True)\n", "labels": {"reads": [{"table": "garmentproduction", "columns": null}], "writes": [{"table": "cities", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO strainlabresults SELECT * FROM legacy\ncur.execute(\"SELECT graphics_mode, co_id FROM emergencies LIMIT 333\")\n", "labels": {"reads": [{"table": "emergencies", "columns": ["graphics_mode", "co_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM circuits\"\n", "labels": {"reads": [{"table": "circuits", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd.dwd_coupon_use_df SELECT sale_date, productiondate, programname FROM broadband_subscribers WHERE sale_date > 75\"\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": ["sale_date", "productiondate", "programname"]}], "writes": [{"table": "dwd.dwd_coupon_use_df", "columns": ["sale_date", "productiondate", "programname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO defensespending SELECT customer_code, investors, course_description, omim FROM product_info WHERE customer_code > 432\"], check=True)\n", "labels": {"reads": [{"table": "product_info", "columns": ["customer_code", "investors", "course_description", "omim"]}], "writes": [{"table": "defensespending", "columns": ["customer_code", "investors", "course_description", "omim"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO professionals (destination_state, production_bopd) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "professionals", "columns": ["destination_state", "production_bopd"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO shipmentinfo SELECT trial_success_rate, hours_served, month FROM monitoring_zones WHERE trial_success_rate > 267\"], check=True)\n", "labels": {"reads": [{"table": "monitoring_zones", "columns": ["trial_success_rate", "hours_served", "month"]}], "writes": [{"table": "shipmentinfo", "columns": ["trial_success_rate", "hours_served", "month"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sculpture_name, salary FROM hall_of_fame LIMIT 270\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO city.community_policing SELECT white, chair_name FROM africa_schema.african_mines WHERE white > 322\")\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": ["sculpture_name", "salary"]}, {"table": "africa_schema.african_mines", "columns": ["white", "chair_name"]}], "writes": [{"table": "city.community_policing", "columns": ["white", "chair_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT base_name, num_projects FROM tourdifferences LIMIT 323\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "tourdifferences", "columns": ["base_name", "num_projects"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO shariah_financing SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table council_tax --target-dir /tmp/land\n", "labels": {"reads": [{"table": "council_tax", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT departure_date, source_u_id FROM dispensaries\", engine)\nimport logging\ndf.to_sql(\"precision_farming_imagery\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dispensaries", "columns": ["departure_date", "source_u_id"]}], "writes": [{"table": "precision_farming_imagery", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"carbon_footprint\")\nsrc.write.insertInto(\"refugees\", overwrite=True)\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": null}], "writes": [{"table": "refugees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO community_development_projects SELECT * FROM legacy\ncur.execute(\"SELECT rating_id, contract_count FROM climate_finance_re LIMIT 389\")\n", "labels": {"reads": [{"table": "climate_finance_re", "columns": ["rating_id", "contract_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.cart_item_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"pediatricians\")\n", "labels": {"reads": [{"table": "stg.cart_item_full", "columns": null}], "writes": [{"table": "pediatricians", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"list\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"recyclednylongarments\")\n", "labels": {"reads": [{"table": "list", "columns": null}], "writes": [{"table": "recyclednylongarments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"agency_satellites\").toPandas()\ndf[[\"aircraft\", \"menu_category\"]].to_sql(\"performance_scores\", engine, index=False)\n", "labels": {"reads": [{"table": "agency_satellites", "columns": null}], "writes": [{"table": "performance_scores", "columns": ["aircraft", "menu_category"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO advocacy SELECT cargo_type, days, attendance_date FROM follows WHERE cargo_type > 357\")\n", "labels": {"reads": [{"table": "follows", "columns": ["cargo_type", "days", "attendance_date"]}], "writes": [{"table": "advocacy", "columns": ["cargo_type", "days", "attendance_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"completed_training\")\nsrc.write.insertInto(\"az_drought_impact\", overwrite=True)\n", "labels": {"reads": [{"table": "completed_training", "columns": null}], "writes": [{"table": "az_drought_impact", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM military_expenditure\", conn)\ndf.to_sql(\"yttrium_production\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "military_expenditure", "columns": null}], "writes": [{"table": "yttrium_production", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"review\")\nwrite_to_output(df, \"dws_events_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "review", "columns": null}], "writes": [{"table": "dws_events_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO textile_suppliers SELECT surname, years_operating, asessment_outcome_code, eventdate FROM tryout WHERE surname > 234\"\n", "labels": {"reads": [{"table": "tryout", "columns": ["surname", "years_operating", "asessment_outcome_code", "eventdate"]}], "writes": [{"table": "textile_suppliers", "columns": ["surname", "years_operating", "asessment_outcome_code", "eventdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO atlantic_plate SELECT a.mailing_date, b.departmentname FROM smartcontracts a JOIN stg.orders_daily b ON a.home_team_points = b.home_team_points\"\n", "labels": {"reads": [{"table": "smartcontracts", "columns": null}, {"table": "stg.orders_daily", "columns": null}], "writes": [{"table": "atlantic_plate", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO call_volume SELECT * FROM legacy\ncur.execute(\"SELECT society, num_sessions FROM stg.stg_risk_score LIMIT 430\")\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": ["society", "num_sessions"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model candidates depends on market\ndbt build --models candidates --vars '{\"src\":\"market\"}'\n", "labels": {"reads": [{"table": "market", "columns": null}], "writes": [{"table": "candidates", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO voting_record SELECT supplychainid, student_id, assessmentdate FROM average WHERE supplychainid > 373\"\n", "labels": {"reads": [{"table": "average", "columns": ["supplychainid", "student_id", "assessmentdate"]}], "writes": [{"table": "voting_record", "columns": ["supplychainid", "student_id", "assessmentdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cerium_production\").toPandas()\ndf[[\"famous_title\", \"strainid\"]].to_sql(\"artcontributors\", engine, index=False)\n", "labels": {"reads": [{"table": "cerium_production", "columns": null}], "writes": [{"table": "artcontributors", "columns": ["famous_title", "strainid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.mart_device_log\", conn)\ndf.to_sql(\"disaster_response_donations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_device_log", "columns": null}], "writes": [{"table": "disaster_response_donations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO training_programs SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO regular_order_products SELECT organic_matter, menuitem FROM us_cities WHERE organic_matter > 253;\nEOF\n", "labels": {"reads": [{"table": "us_cities", "columns": ["organic_matter", "menuitem"]}], "writes": [{"table": "regular_order_products", "columns": ["organic_matter", "menuitem"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO autoshow SELECT damage_millions_usd, document_id, worker_id FROM crop_yield WHERE damage_millions_usd > 206;\nEOF\n", "labels": {"reads": [{"table": "crop_yield", "columns": ["damage_millions_usd", "document_id", "worker_id"]}], "writes": [{"table": "autoshow", "columns": ["damage_millions_usd", "document_id", "worker_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO classrooms SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT school_colors, offender_name FROM dw.shipments_df LIMIT 69\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": ["school_colors", "offender_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO economic_diversification SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO productsafety SELECT * FROM legacy\ncur.execute(\"SELECT clientid, owner FROM ads.ads_device_log_di LIMIT 308\")\n", "labels": {"reads": [{"table": "ads.ads_device_log_di", "columns": ["clientid", "owner"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.member_name > 407).all()\n# src table: eu_data_usage\nengine.execute(\"INSERT INTO wind_projects SELECT * FROM eu_data_usage\")\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": null}], "writes": [{"table": "wind_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM farmers_india\"\n", "labels": {"reads": [{"table": "farmers_india", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM galleries\", conn)\ndf.to_sql(\"ods.member_point_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "galleries", "columns": null}], "writes": [{"table": "ods.member_point_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO clinic_2022 (court_appearances, donorage) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "clinic_2022", "columns": ["court_appearances", "donorage"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"operation\")\nsrc.write.insertInto(\"audience_demographics\", overwrite=True)\n", "labels": {"reads": [{"table": "operation", "columns": null}], "writes": [{"table": "audience_demographics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO biotech_startups SELECT * FROM legacy\ncur.execute(\"SELECT theatrename, productionid FROM menu LIMIT 493\")\n", "labels": {"reads": [{"table": "menu", "columns": ["theatrename", "productionid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO county_public_safety SELECT resolved, quantity_sold, post_date FROM tech_volunteers WHERE resolved > 355;\nEOF\n", "labels": {"reads": [{"table": "tech_volunteers", "columns": ["resolved", "quantity_sold", "post_date"]}], "writes": [{"table": "county_public_safety", "columns": ["resolved", "quantity_sold", "post_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO geneva_motor_show SELECT dish, genrename, furniture_id, report_type FROM stg.stg_risk_score WHERE dish > 277\"\n", "labels": {"reads": [{"table": "stg.stg_risk_score", "columns": ["dish", "genrename", "furniture_id", "report_type"]}], "writes": [{"table": "geneva_motor_show", "columns": ["dish", "genrename", "furniture_id", "report_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.exposure_di\").toPandas()\ndf[[\"email_address\", \"connection\"]].to_sql(\"models_safety\", engine, index=False)\n", "labels": {"reads": [{"table": "dw.exposure_di", "columns": null}], "writes": [{"table": "models_safety", "columns": ["email_address", "connection"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO investors SELECT police_force, special_features FROM classicgame WHERE police_force > 282;\nEOF\n", "labels": {"reads": [{"table": "classicgame", "columns": ["police_force", "special_features"]}], "writes": [{"table": "investors", "columns": ["police_force", "special_features"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT support_rate, dockingid FROM cloud_issues LIMIT 88\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "cloud_issues", "columns": ["support_rate", "dockingid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ocean_pollution SELECT asset_id, animal_species FROM subjects WHERE asset_id > 182\"\n", "labels": {"reads": [{"table": "subjects", "columns": ["asset_id", "animal_species"]}], "writes": [{"table": "ocean_pollution", "columns": ["asset_id", "animal_species"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT days, goal_id FROM immunizationrates\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"cargo_equipment\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "immunizationrates", "columns": ["days", "goal_id"]}], "writes": [{"table": "cargo_equipment", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergency_categories\").toPandas()\ndf[[\"claimtype\", \"fiscal_year\"]].to_sql(\"redundant_billing_data\", engine, index=False)\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": [{"table": "redundant_billing_data", "columns": ["claimtype", "fiscal_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO faculty_participates_in SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.refunds_delta SELECT * FROM legacy\ncur.execute(\"SELECT classid, volunteername FROM furniture LIMIT 39\")\n", "labels": {"reads": [{"table": "furniture", "columns": ["classid", "volunteername"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO safety_research SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ocean_species\"\n", "labels": {"reads": [{"table": "ocean_species", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO cuisine SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT section_title, card_type_code FROM crops_year LIMIT 46;\nINSERT INTO artcontributors SELECT attribute_id, stationname, discount FROM revenue WHERE attribute_id > 93;\nSQL\n", "labels": {"reads": [{"table": "crops_year", "columns": ["section_title", "card_type_code"]}, {"table": "revenue", "columns": ["attribute_id", "stationname", "discount"]}], "writes": [{"table": "artcontributors", "columns": ["attribute_id", "stationname", "discount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ship\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"habitat3\")\n", "labels": {"reads": [{"table": "ship", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sfc_articles SELECT preference_rating, destination, has_spf FROM aquatic_farms WHERE preference_rating > 112\"\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["preference_rating", "destination", "has_spf"]}], "writes": [{"table": "sfc_articles", "columns": ["preference_rating", "destination", "has_spf"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bioprocess_engineering SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO factory_workers SELECT purchase_id, online_dispute_resolution, mean_temperature_f FROM operations WHERE purchase_id > 104\"\n", "labels": {"reads": [{"table": "operations", "columns": ["purchase_id", "online_dispute_resolution", "mean_temperature_f"]}], "writes": [{"table": "factory_workers", "columns": ["purchase_id", "online_dispute_resolution", "mean_temperature_f"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT line_name, ngo_id FROM autonomousvehicleaccidents LIMIT 43;\nINSERT INTO appointments SELECT value_points, starting_year, dorm_name, health_equity_metric_3 FROM threats WHERE value_points > 61;\nSQL\n", "labels": {"reads": [{"table": "autonomousvehicleaccidents", "columns": ["line_name", "ngo_id"]}, {"table": "threats", "columns": ["value_points", "starting_year", "dorm_name", "health_equity_metric_3"]}], "writes": [{"table": "appointments", "columns": ["value_points", "starting_year", "dorm_name", "health_equity_metric_3"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO check_ins SELECT status_code, users_engaged, call_time FROM veteran_unemployment WHERE status_code > 471\"\n", "labels": {"reads": [{"table": "veteran_unemployment", "columns": ["status_code", "users_engaged", "call_time"]}], "writes": [{"table": "check_ins", "columns": ["status_code", "users_engaged", "call_time"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO epl_teams SELECT a.musical_id, b.products_last_year FROM dispensaries a JOIN video_games b ON a.home_team = b.home_team\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}, {"table": "video_games", "columns": null}], "writes": [{"table": "epl_teams", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO food_safety_inspections SELECT algorithmic_fairness_score, billing_amount FROM contract_negotiations_un WHERE algorithmic_fairness_score > 254\"\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": ["algorithmic_fairness_score", "billing_amount"]}], "writes": [{"table": "food_safety_inspections", "columns": ["algorithmic_fairness_score", "billing_amount"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"veteran_employment\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "veteran_employment", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO building_stats SELECT * FROM legacy\ncur.execute(\"SELECT strain_type, session_language FROM unions LIMIT 245\")\n", "labels": {"reads": [{"table": "unions", "columns": ["strain_type", "session_language"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO drought_impact (destroyed_by_employee_id, strain) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "drought_impact", "columns": ["destroyed_by_employee_id", "strain"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT content_id, contributions FROM sustainablebrands LIMIT 450\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": ["content_id", "contributions"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.threat > 268).all()\n# src table: tourism\nengine.execute(\"INSERT INTO defense_spending SELECT * FROM tourism\")\n", "labels": {"reads": [{"table": "tourism", "columns": null}], "writes": [{"table": "defense_spending", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO providers (excavation_site, all_home) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "providers", "columns": ["excavation_site", "all_home"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ocean_pollution SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO new_schedules SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO smartcontracts SELECT satellite_id, habitat, equipment_id FROM ai_systems WHERE satellite_id > 283\"\n", "labels": {"reads": [{"table": "ai_systems", "columns": ["satellite_id", "habitat", "equipment_id"]}], "writes": [{"table": "smartcontracts", "columns": ["satellite_id", "habitat", "equipment_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ytterbiumproduction SELECT residence, friend, claim_id, seat_section FROM volunteerhours WHERE residence > 221\"\n", "labels": {"reads": [{"table": "volunteerhours", "columns": ["residence", "friend", "claim_id", "seat_section"]}], "writes": [{"table": "ytterbiumproduction", "columns": ["residence", "friend", "claim_id", "seat_section"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO engineer_skills SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"safe_dataset\")\nsrc.write.insertInto(\"brandrevenue\", overwrite=True)\n", "labels": {"reads": [{"table": "safe_dataset", "columns": null}], "writes": [{"table": "brandrevenue", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dws_orders_full depends on militarycyberops\ndbt build --select dws_orders_full --vars '{\"source_table\":\"militarycyberops\"}'\n", "labels": {"reads": [{"table": "militarycyberops", "columns": null}], "writes": [{"table": "dws_orders_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.mart_risk_score_hourly (room_number, facid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_risk_score_hourly", "columns": ["room_number", "facid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO artist_demographics SELECT union_member_id, stageposition, hotel_chain_name, runtime FROM mining_operations WHERE union_member_id > 289\")\n", "labels": {"reads": [{"table": "mining_operations", "columns": ["union_member_id", "stageposition", "hotel_chain_name", "runtime"]}], "writes": [{"table": "artist_demographics", "columns": ["union_member_id", "stageposition", "hotel_chain_name", "runtime"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM production_rare_earth_elements\", conn)\ndf.to_sql(\"intelligenceoperations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": null}], "writes": [{"table": "intelligenceoperations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO inst SELECT * FROM legacy\ncur.execute(\"SELECT area_size, nurse FROM vehicle_data LIMIT 267\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": ["area_size", "nurse"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.population > 451).all()\n# src table: plants\nengine.execute(\"INSERT INTO international_visitors SELECT * FROM plants\")\n", "labels": {"reads": [{"table": "plants", "columns": null}], "writes": [{"table": "international_visitors", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_master_index\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"phone_market\")\n", "labels": {"reads": [{"table": "customer_master_index", "columns": null}], "writes": [{"table": "phone_market", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO model_fairness SELECT frameworkcountry, stu_dob, membership_amount FROM artpieces WHERE frameworkcountry > 186\"\n", "labels": {"reads": [{"table": "artpieces", "columns": ["frameworkcountry", "stu_dob", "membership_amount"]}], "writes": [{"table": "model_fairness", "columns": ["frameworkcountry", "stu_dob", "membership_amount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO platformh SELECT incident_category, emp_jobcode FROM program_history WHERE incident_category > 147\"\n", "labels": {"reads": [{"table": "program_history", "columns": ["incident_category", "emp_jobcode"]}], "writes": [{"table": "platformh", "columns": ["incident_category", "emp_jobcode"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO strategies SELECT violation_count, half FROM train_maintenance WHERE violation_count > 302\"\n", "labels": {"reads": [{"table": "train_maintenance", "columns": ["violation_count", "half"]}], "writes": [{"table": "strategies", "columns": ["violation_count", "half"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO disabilitysupportprograms SELECT unit_of_measure, player_id FROM vehicle_registrations WHERE unit_of_measure > 222;\nEOF\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": ["unit_of_measure", "player_id"]}], "writes": [{"table": "disabilitysupportprograms", "columns": ["unit_of_measure", "player_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO stg.stg_device_log_daily SELECT developer, shipping_agent_code, last_workout_date, customer_status_code FROM professional_development WHERE developer > 450\")\n", "labels": {"reads": [{"table": "professional_development", "columns": ["developer", "shipping_agent_code", "last_workout_date", "customer_status_code"]}], "writes": [{"table": "stg.stg_device_log_daily", "columns": ["developer", "shipping_agent_code", "last_workout_date", "customer_status_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM movies\"\n", "labels": {"reads": [{"table": "movies", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO water_conservation SELECT popularity, fleet_id, elevation, implemented_date FROM disaster_mitigation WHERE popularity > 144\"\n", "labels": {"reads": [{"table": "disaster_mitigation", "columns": ["popularity", "fleet_id", "elevation", "implemented_date"]}], "writes": [{"table": "water_conservation", "columns": ["popularity", "fleet_id", "elevation", "implemented_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_hours\").toPandas()\ndf[[\"billing_city\", \"incident_description\"]].to_sql(\"infrastructure\", engine, index=False)\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": [{"table": "infrastructure", "columns": ["billing_city", "incident_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table esa_missions --columns eia_date,pressure --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "esa_missions", "columns": ["eia_date", "pressure"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"department_stores\").toPandas()\ndf[[\"part_name\", \"opened_date\"]].to_sql(\"mental_health_professionals_2\", engine, index=False)\n", "labels": {"reads": [{"table": "department_stores", "columns": null}], "writes": [{"table": "mental_health_professionals_2", "columns": ["part_name", "opened_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.dwd_payments_di (sale_price, labor_cost) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["sale_price", "labor_cost"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stocking_density, committee FROM climate_data\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"investor_activities\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "climate_data", "columns": ["stocking_density", "committee"]}], "writes": [{"table": "investor_activities", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donor_country, case_id FROM mart.shipments_full LIMIT 94\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "mart.shipments_full", "columns": ["donor_country", "case_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO station_crime_rates SELECT register_year, completed_course, consider_rate FROM protein WHERE register_year > 296\")\n", "labels": {"reads": [{"table": "protein", "columns": ["register_year", "completed_course", "consider_rate"]}], "writes": [{"table": "station_crime_rates", "columns": ["register_year", "completed_course", "consider_rate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO trainings SELECT cost, school, journalist_id FROM humanitarian_aid WHERE cost > 82\"\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["cost", "school", "journalist_id"]}], "writes": [{"table": "trainings", "columns": ["cost", "school", "journalist_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO chemical_processes SELECT a.facility_name, b.start_station_name FROM un_peacekeeping_operations a JOIN whale_sightings b ON a.pixels = b.pixels\"\n", "labels": {"reads": [{"table": "un_peacekeeping_operations", "columns": null}, {"table": "whale_sightings", "columns": null}], "writes": [{"table": "chemical_processes", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT complaintid, digital FROM dwd.dwd_device_log_delta LIMIT 439;\nINSERT INTO investment_rounds SELECT room, pcp, spacecraft FROM ads.ads_risk_score_hourly WHERE room > 162;\nSQL\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": ["complaintid", "digital"]}, {"table": "ads.ads_risk_score_hourly", "columns": ["room", "pcp", "spacecraft"]}], "writes": [{"table": "investment_rounds", "columns": ["room", "pcp", "spacecraft"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT market_rate, name_first FROM news_report LIMIT 427;\nINSERT INTO workplace_safety SELECT restypedescription, mediatorid, participation_date FROM climateresearch WHERE restypedescription > 79;\nSQL\n", "labels": {"reads": [{"table": "news_report", "columns": ["market_rate", "name_first"]}, {"table": "climateresearch", "columns": ["restypedescription", "mediatorid", "participation_date"]}], "writes": [{"table": "workplace_safety", "columns": ["restypedescription", "mediatorid", "participation_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO region SELECT date_incident_start, event_id, scoreid FROM shared_escooters WHERE date_incident_start > 154\"\n", "labels": {"reads": [{"table": "shared_escooters", "columns": ["date_incident_start", "event_id", "scoreid"]}], "writes": [{"table": "region", "columns": ["date_incident_start", "event_id", "scoreid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT enrollment_date, occupancy_rate FROM eventattendance LIMIT 148\")\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO advisor SELECT innovation_id, strain FROM hydro_power WHERE innovation_id > 484\")\n", "labels": {"reads": [{"table": "eventattendance", "columns": ["enrollment_date", "occupancy_rate"]}, {"table": "hydro_power", "columns": ["innovation_id", "strain"]}], "writes": [{"table": "advisor", "columns": ["innovation_id", "strain"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO labor_cost SELECT a.catalog_level_name, b.company_id FROM cosmetic_sales a JOIN communityengagementmetrics b ON a.coownerid = b.coownerid\"\n", "labels": {"reads": [{"table": "cosmetic_sales", "columns": null}, {"table": "communityengagementmetrics", "columns": null}], "writes": [{"table": "labor_cost", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO reviews SELECT unit_price, end_date FROM program_outcomes WHERE unit_price > 295\"\n", "labels": {"reads": [{"table": "program_outcomes", "columns": ["unit_price", "end_date"]}], "writes": [{"table": "reviews", "columns": ["unit_price", "end_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO shared_ebikes SELECT * FROM legacy\ncur.execute(\"SELECT offender_name, investment_name FROM seal_population LIMIT 35\")\n", "labels": {"reads": [{"table": "seal_population", "columns": ["offender_name", "investment_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT journalist_id, oct FROM membership_data LIMIT 244;\nINSERT INTO total_consumption SELECT pediatrician_id, cases_handled FROM impact_asia WHERE pediatrician_id > 355;\nSQL\n", "labels": {"reads": [{"table": "membership_data", "columns": ["journalist_id", "oct"]}, {"table": "impact_asia", "columns": ["pediatrician_id", "cases_handled"]}], "writes": [{"table": "total_consumption", "columns": ["pediatrician_id", "cases_handled"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO algorithmic_fairness (pallet_id, scientist) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "algorithmic_fairness", "columns": ["pallet_id", "scientist"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sponsorship_donations (platform, outcome_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sponsorship_donations", "columns": ["platform", "outcome_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO virtual_tour_engagement SELECT price_in_dollars, plan_id FROM emergencyservices WHERE price_in_dollars > 26\"\n", "labels": {"reads": [{"table": "emergencyservices", "columns": ["price_in_dollars", "plan_id"]}], "writes": [{"table": "virtual_tour_engagement", "columns": ["price_in_dollars", "plan_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"attorneylocationyear\")\nsrc.write.insertInto(\"stats\", overwrite=True)\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": null}], "writes": [{"table": "stats", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO policyadvocacyevents (advisor, quantity_sold) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "policyadvocacyevents", "columns": ["advisor", "quantity_sold"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"workforce_training\")\nsrc.write.insertInto(\"rnd_budget\", overwrite=True)\n", "labels": {"reads": [{"table": "workforce_training", "columns": null}], "writes": [{"table": "rnd_budget", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO teams_mascots SELECT hometown, date_of_notes, crispr_id FROM site WHERE hometown > 230;\nEOF\n", "labels": {"reads": [{"table": "site", "columns": ["hometown", "date_of_notes", "crispr_id"]}], "writes": [{"table": "teams_mascots", "columns": ["hometown", "date_of_notes", "crispr_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO refugees (quality_rank, account_balance) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "refugees", "columns": ["quality_rank", "account_balance"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO mart.mart_payments_hourly SELECT policy_number, num_owners FROM reporters WHERE policy_number > 338\"\n", "labels": {"reads": [{"table": "reporters", "columns": ["policy_number", "num_owners"]}], "writes": [{"table": "mart.mart_payments_hourly", "columns": ["policy_number", "num_owners"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bank SELECT emp_num, attendeeid, round FROM dwd.dwd_events_delta WHERE emp_num > 353\"\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": ["emp_num", "attendeeid", "round"]}], "writes": [{"table": "bank", "columns": ["emp_num", "attendeeid", "round"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO news_reporting SELECT * FROM legacy\ncur.execute(\"SELECT max_depth, popularity FROM mars_rovers LIMIT 492\")\n", "labels": {"reads": [{"table": "mars_rovers", "columns": ["max_depth", "popularity"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.ods_campaigns_df\", conn)\ndf.to_sql(\"materials\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_df", "columns": null}], "writes": [{"table": "materials", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model innovation_grants depends on vessels_2\ndbt build --select innovation_grants --vars '{\"src\":\"vessels_2\"}'\n", "labels": {"reads": [{"table": "vessels_2", "columns": null}], "writes": [{"table": "innovation_grants", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table product_ingredient --target-dir /tmp/land\n", "labels": {"reads": [{"table": "product_ingredient", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.stg_coupon_use_hourly SELECT use_date, culturalcompetency FROM forests WHERE use_date > 174\"\n", "labels": {"reads": [{"table": "forests", "columns": ["use_date", "culturalcompetency"]}], "writes": [{"table": "stg.stg_coupon_use_hourly", "columns": ["use_date", "culturalcompetency"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.address > 480).all()\n# src table: diversity\nengine.execute(\"INSERT INTO region_stats SELECT * FROM diversity\")\n", "labels": {"reads": [{"table": "diversity", "columns": null}], "writes": [{"table": "region_stats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"malicious_activity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "malicious_activity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO explainableai SELECT community_size, satellite_name, volume_id FROM problem_log WHERE community_size > 274\")\n", "labels": {"reads": [{"table": "problem_log", "columns": ["community_size", "satellite_name", "volume_id"]}], "writes": [{"table": "explainableai", "columns": ["community_size", "satellite_name", "volume_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.dwd_payments_di (machine, permitid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["machine", "permitid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT platform_id, sustainable_practice FROM vehicledata LIMIT 345;\nINSERT INTO arcticocean SELECT sellingprice, don_name, hoursspent, inspectionscore FROM virtual_visitors WHERE sellingprice > 107;\nSQL\n", "labels": {"reads": [{"table": "vehicledata", "columns": ["platform_id", "sustainable_practice"]}, {"table": "virtual_visitors", "columns": ["sellingprice", "don_name", "hoursspent", "inspectionscore"]}], "writes": [{"table": "arcticocean", "columns": ["sellingprice", "don_name", "hoursspent", "inspectionscore"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bi_sessions_hourly depends on virtual_tours\ndbt build --select bi_sessions_hourly --vars '{\"src\":\"virtual_tours\"}'\n", "labels": {"reads": [{"table": "virtual_tours", "columns": null}], "writes": [{"table": "bi_sessions_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_payments_delta SELECT a.mid, b.number_cities FROM dwd.dwd_risk_score_delta a JOIN sustainable_urban b ON a.inspectionscore = b.inspectionscore\"\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}, {"table": "sustainable_urban", "columns": null}], "writes": [{"table": "ads.ads_payments_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO autonomousdriving SELECT a.max_salary, b.total_distance FROM ads.ads_users_hourly a JOIN vessel_tracking b ON a.shipping_mode = b.shipping_mode\"\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": null}, {"table": "vessel_tracking", "columns": null}], "writes": [{"table": "autonomousdriving", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table all_programs --columns trainingdate,extractiondate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "all_programs", "columns": ["trainingdate", "extractiondate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads (daily_distance, studio) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads", "columns": ["daily_distance", "studio"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climber\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "climber", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.exposure_di\").toPandas()\ndf[[\"waste_type\", \"rig_name\"]].to_sql(\"bi.inventory_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "dw.exposure_di", "columns": null}], "writes": [{"table": "bi.inventory_delta", "columns": ["waste_type", "rig_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dysprosium_prod, crop FROM stg.stg_risk_score_hourly\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"matches\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": ["dysprosium_prod", "crop"]}], "writes": [{"table": "matches", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"mart.mart_refunds_di\")\nupsert_to_store(df, \"bi.bi_campaigns_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": null}], "writes": [{"table": "bi.bi_campaigns_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM seamounts\", conn)\ndf.to_sql(\"ethicalaibudget\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": [{"table": "ethicalaibudget", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_products_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"ai_systems\")\nsink_to_sink(df, \"cosmetic_sales\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ai_systems", "columns": null}], "writes": [{"table": "cosmetic_sales", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT rural_area, age_group_id FROM workplaces LIMIT 137\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "workplaces", "columns": ["rural_area", "age_group_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"renewable_energy_investments\")\nsrc.write.insertInto(\"global_tournament\", overwrite=True)\n", "labels": {"reads": [{"table": "renewable_energy_investments", "columns": null}], "writes": [{"table": "global_tournament", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_renewable_energy\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"startup_founders\")\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": null}], "writes": [{"table": "startup_founders", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO forests SELECT * FROM legacy\ncur.execute(\"SELECT points_per_game, kills FROM port_visits LIMIT 268\")\n", "labels": {"reads": [{"table": "port_visits", "columns": ["points_per_game", "kills"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM discount_coupons\"\n", "labels": {"reads": [{"table": "discount_coupons", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO artists_valuation SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO donations2022 SELECT image_date, ll_hours, volunteerjoindate, org_id FROM wells WHERE image_date > 271\"\n", "labels": {"reads": [{"table": "wells", "columns": ["image_date", "ll_hours", "volunteerjoindate", "org_id"]}], "writes": [{"table": "donations2022", "columns": ["image_date", "ll_hours", "volunteerjoindate", "org_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT location, sales_id FROM complaints LIMIT 214\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "complaints", "columns": ["location", "sales_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO rural_resources SELECT post_date, opid, replacement_cost FROM trade_history WHERE post_date > 489\"\n", "labels": {"reads": [{"table": "trade_history", "columns": ["post_date", "opid", "replacement_cost"]}], "writes": [{"table": "rural_resources", "columns": ["post_date", "opid", "replacement_cost"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT built, account_id FROM infantmortalitydata LIMIT 500\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": ["built", "account_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safety_incidents\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"performances\")\n", "labels": {"reads": [{"table": "safety_incidents", "columns": null}], "writes": [{"table": "performances", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ods.shipments_df SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO electric_vehicle_stats SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO event_attendance (circuitid, staystart) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "event_attendance", "columns": ["circuitid", "staystart"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM captain\"\n", "labels": {"reads": [{"table": "captain", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT customername, attraction_type_description FROM supportservices\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"bias_categories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "supportservices", "columns": ["customername", "attraction_type_description"]}], "writes": [{"table": "bias_categories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT userid, feedid FROM recalls\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"stg.risk_score_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "recalls", "columns": ["userid", "feedid"]}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.other_details > 378).all()\n# src table: market\nengine.execute(\"INSERT INTO workforce_training SELECT * FROM market\")\n", "labels": {"reads": [{"table": "market", "columns": null}], "writes": [{"table": "workforce_training", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO plots SELECT channel_code, brandid, certification FROM artifact_analysis WHERE channel_code > 498\")\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": ["channel_code", "brandid", "certification"]}], "writes": [{"table": "plots", "columns": ["channel_code", "brandid", "certification"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO waterconsumptionbyoperation SELECT contributorid, lawyer_name, fair_trade, country FROM space_agencies_2 WHERE contributorid > 263\"\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": ["contributorid", "lawyer_name", "fair_trade", "country"]}], "writes": [{"table": "waterconsumptionbyoperation", "columns": ["contributorid", "lawyer_name", "fair_trade", "country"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_budgets\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "city_budgets", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM environmental_impact\", conn)\ndf.to_sql(\"community_policing_events\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "environmental_impact", "columns": null}], "writes": [{"table": "community_policing_events", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO government.region SELECT a.participant_count, b.countryid FROM authenticationlogs a JOIN audience b ON a.workout_name = b.workout_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "authenticationlogs", "columns": null}, {"table": "audience", "columns": null}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO chemical_processes SELECT airport_name, complaint_status_code FROM ods.ods_clicks_di WHERE airport_name > 173\")\n", "labels": {"reads": [{"table": "ods.ods_clicks_di", "columns": ["airport_name", "complaint_status_code"]}], "writes": [{"table": "chemical_processes", "columns": ["airport_name", "complaint_status_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_usage\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"government_funding\")\n", "labels": {"reads": [{"table": "water_usage", "columns": null}], "writes": [{"table": "government_funding", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart_refunds_delta SELECT a.network_name, b.adoption_date FROM securityincidents a JOIN athletes b ON a.social_impact_score = b.social_impact_score\"\n", "labels": {"reads": [{"table": "securityincidents", "columns": null}, {"table": "athletes", "columns": null}], "writes": [{"table": "mart_refunds_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO police_stations SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_fabrics\").toPandas()\ndf[[\"regional_population\", \"directed_by\"]].to_sql(\"investments\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": null}], "writes": [{"table": "investments", "columns": ["regional_population", "directed_by"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO crime_reports (vaccinations, college_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "crime_reports", "columns": ["vaccinations", "college_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi_shipments_daily SELECT company_type_code, claim_amount, contact_number FROM safetytestingcounts WHERE company_type_code > 129\"\n", "labels": {"reads": [{"table": "safetytestingcounts", "columns": ["company_type_code", "claim_amount", "contact_number"]}], "writes": [{"table": "bi_shipments_daily", "columns": ["company_type_code", "claim_amount", "contact_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_vendors_di (therapy_id, recycler_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_vendors_di", "columns": ["therapy_id", "recycler_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM agricultural_projects\", conn)\ndf.to_sql(\"gardens\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "agricultural_projects", "columns": null}], "writes": [{"table": "gardens", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"ads.ads_products_hourly\")\nsave_to_store(df, \"concert_revenue\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.ads_products_hourly", "columns": null}], "writes": [{"table": "concert_revenue", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO investors SELECT bookings, quantitysold, visitorid FROM apac_hotel_views WHERE bookings > 401\")\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["bookings", "quantitysold", "visitorid"]}], "writes": [{"table": "investors", "columns": ["bookings", "quantitysold", "visitorid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nsql = \"INSERT INTO skincare_sales SELECT a.initiativeid, b.assistingnurse FROM marine_conservation a JOIN minor_in b ON a.is_recycled = b.is_recycled\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "marine_conservation", "columns": null}, {"table": "minor_in", "columns": null}], "writes": [{"table": "skincare_sales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ticket_sales SELECT completion_date, staff_last_name, transaction_volume, business_size FROM organization_contact_individuals WHERE completion_date > 323\"\n", "labels": {"reads": [{"table": "organization_contact_individuals", "columns": ["completion_date", "staff_last_name", "transaction_volume", "business_size"]}], "writes": [{"table": "ticket_sales", "columns": ["completion_date", "staff_last_name", "transaction_volume", "business_size"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO dwd.events_daily SELECT a.survey_id, b.emp_id FROM causes a JOIN submersible_dives b ON a.vehicle_details = b.vehicle_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "causes", "columns": null}, {"table": "submersible_dives", "columns": null}], "writes": [{"table": "dwd.events_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO species_data SELECT * FROM legacy\ncur.execute(\"SELECT online_dispute_resolution, protected FROM categories LIMIT 195\")\n", "labels": {"reads": [{"table": "categories", "columns": ["online_dispute_resolution", "protected"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO development_hours (trip_type, word_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "development_hours", "columns": ["trip_type", "word_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO shariah_compliant_loans SELECT neighborhood_id, workout_id, sensor_reading FROM workshops WHERE neighborhood_id > 68;\nEOF\n", "labels": {"reads": [{"table": "workshops", "columns": ["neighborhood_id", "workout_id", "sensor_reading"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["neighborhood_id", "workout_id", "sensor_reading"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gradeconversion\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"defense_diplomacy\")\n", "labels": {"reads": [{"table": "gradeconversion", "columns": null}], "writes": [{"table": "defense_diplomacy", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table marketingbudget --target-dir /tmp/land\n", "labels": {"reads": [{"table": "marketingbudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table healthcare_system --columns host_city,workshop_group_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "healthcare_system", "columns": ["host_city", "workshop_group_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"funding_rounds\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"genderdistribution\")\n", "labels": {"reads": [{"table": "funding_rounds", "columns": null}], "writes": [{"table": "genderdistribution", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table habitat --columns amount_due,excavation_site --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "habitat", "columns": ["amount_due", "excavation_site"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO donation SELECT budgetid, round_number FROM captain WHERE budgetid > 234\"\n", "labels": {"reads": [{"table": "captain", "columns": ["budgetid", "round_number"]}], "writes": [{"table": "donation", "columns": ["budgetid", "round_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cb_agreements --columns funding_amount,stu_dob --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cb_agreements", "columns": ["funding_amount", "stu_dob"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"haircare_cruelty\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tourismproviders\")\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}], "writes": [{"table": "tourismproviders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.don_name > 369).all()\n# src table: education_programs\nengine.execute(\"INSERT INTO water_sources SELECT * FROM education_programs\")\n", "labels": {"reads": [{"table": "education_programs", "columns": null}], "writes": [{"table": "water_sources", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO premises (mediatorid, fair_trade) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "premises", "columns": ["mediatorid", "fair_trade"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO species_observations SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM exit_strategy\"\n", "labels": {"reads": [{"table": "exit_strategy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_justice_orgs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stageposition, marketing_region_code FROM customer_events\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"county\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customer_events", "columns": ["stageposition", "marketing_region_code"]}], "writes": [{"table": "county", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT product_subcategory, physician FROM hotel_ratings LIMIT 166;\nINSERT INTO ref_incident_type SELECT votes, appelation FROM agro_regions WHERE votes > 113;\nSQL\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": ["product_subcategory", "physician"]}, {"table": "agro_regions", "columns": ["votes", "appelation"]}], "writes": [{"table": "ref_incident_type", "columns": ["votes", "appelation"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"course_attendance\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart_shipments_full\")\n", "labels": {"reads": [{"table": "course_attendance", "columns": null}], "writes": [{"table": "mart_shipments_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model musicgenre depends on dwd.dwd_payments\ndbt build --models musicgenre --vars '{\"source_table\":\"dwd.dwd_payments\"}'\n", "labels": {"reads": [{"table": "dwd.dwd_payments", "columns": null}], "writes": [{"table": "musicgenre", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT algorithmic_fairness_score, rehab_date FROM healthequitymetrics LIMIT 184\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "healthequitymetrics", "columns": ["algorithmic_fairness_score", "rehab_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM police_stations\"\n", "labels": {"reads": [{"table": "police_stations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM tracklists\"\n", "labels": {"reads": [{"table": "tracklists", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ref_locations SELECT competition_type, offender_name, artist_gender FROM community_programs WHERE competition_type > 369;\nEOF\n", "labels": {"reads": [{"table": "community_programs", "columns": ["competition_type", "offender_name", "artist_gender"]}], "writes": [{"table": "ref_locations", "columns": ["competition_type", "offender_name", "artist_gender"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT address_road, dept_store_id FROM language LIMIT 173\")\nimport logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO salinity_readings SELECT farm_name, allocation_type, aid_id, staff_gender FROM mining_operations WHERE farm_name > 169\")\n", "labels": {"reads": [{"table": "language", "columns": ["address_road", "dept_store_id"]}, {"table": "mining_operations", "columns": ["farm_name", "allocation_type", "aid_id", "staff_gender"]}], "writes": [{"table": "salinity_readings", "columns": ["farm_name", "allocation_type", "aid_id", "staff_gender"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT gross_worldwide, vehicle FROM assignedto LIMIT 43\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO station_company SELECT operating_system, model_id, official_name FROM game_results WHERE operating_system > 136\")\n", "labels": {"reads": [{"table": "assignedto", "columns": ["gross_worldwide", "vehicle"]}, {"table": "game_results", "columns": ["operating_system", "model_id", "official_name"]}], "writes": [{"table": "station_company", "columns": ["operating_system", "model_id", "official_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO asset_parts SELECT dish_id, manufacturer_name, distance, customer_status_code FROM waste_generation_city_v2 WHERE dish_id > 165\"\n", "labels": {"reads": [{"table": "waste_generation_city_v2", "columns": ["dish_id", "manufacturer_name", "distance", "customer_status_code"]}], "writes": [{"table": "asset_parts", "columns": ["dish_id", "manufacturer_name", "distance", "customer_status_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO culturalpractices SELECT attendee_id, partid FROM immunization WHERE attendee_id > 403\"\n", "labels": {"reads": [{"table": "immunization", "columns": ["attendee_id", "partid"]}], "writes": [{"table": "culturalpractices", "columns": ["attendee_id", "partid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO vessel SELECT professionalid, test_result, timestamp FROM temperature_data WHERE professionalid > 419\"\n", "labels": {"reads": [{"table": "temperature_data", "columns": ["professionalid", "test_result", "timestamp"]}], "writes": [{"table": "vessel", "columns": ["professionalid", "test_result", "timestamp"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM hospitallocations\"\n", "labels": {"reads": [{"table": "hospitallocations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO supplier_ethics SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mappinglengths --columns astronaut,restaurant --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mappinglengths", "columns": ["astronaut", "restaurant"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.coupon_use\", conn)\ndf.to_sql(\"salinity_readings\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.coupon_use", "columns": null}], "writes": [{"table": "salinity_readings", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO deep_sea_expeditions SELECT period, flno, subscriber_id, county_name FROM student WHERE period > 282\"\n", "labels": {"reads": [{"table": "student", "columns": ["period", "flno", "subscriber_id", "county_name"]}], "writes": [{"table": "deep_sea_expeditions", "columns": ["period", "flno", "subscriber_id", "county_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"virtual_tours_oceania\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "virtual_tours_oceania", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT extraction_state, area_id FROM menuitems LIMIT 447;\nINSERT INTO textileworkers SELECT price, strain_name, local_authority, grant_name FROM customer WHERE price > 297;\nSQL\n", "labels": {"reads": [{"table": "menuitems", "columns": ["extraction_state", "area_id"]}, {"table": "customer", "columns": ["price", "strain_name", "local_authority", "grant_name"]}], "writes": [{"table": "textileworkers", "columns": ["price", "strain_name", "local_authority", "grant_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO food_items SELECT a.royal_family_details, b.contact_number FROM water_distribution a JOIN carbon_pricing b ON a.investment_date = b.investment_date\"\n", "labels": {"reads": [{"table": "water_distribution", "columns": null}, {"table": "carbon_pricing", "columns": null}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM facility_production\", conn)\ndf.to_sql(\"public.developers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "facility_production", "columns": null}], "writes": [{"table": "public.developers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.ods_users_di SELECT * FROM legacy\ncur.execute(\"SELECT sales_details, games FROM workforcediversity LIMIT 4\")\n", "labels": {"reads": [{"table": "workforcediversity", "columns": ["sales_details", "games"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO esports_teams SELECT a.bedtype, b.is_organic FROM contract_negotiations a JOIN textile_waste b ON a.port_name = b.port_name\"\n", "labels": {"reads": [{"table": "contract_negotiations", "columns": null}, {"table": "textile_waste", "columns": null}], "writes": [{"table": "esports_teams", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO accessibility_audits SELECT refugee_id, metric_id, organisation_details FROM mental_health_parity_violations WHERE refugee_id > 477\"\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": ["refugee_id", "metric_id", "organisation_details"]}], "writes": [{"table": "accessibility_audits", "columns": ["refugee_id", "metric_id", "organisation_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"humanitarian_aid\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"recycledmaterialsgarments\")\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": null}], "writes": [{"table": "recycledmaterialsgarments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO displaced_people (ai_customer_service, checkout) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "displaced_people", "columns": ["ai_customer_service", "checkout"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO satellitematerials SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"danceevents\").toPandas()\ndf[[\"cid\", \"prereq_id\"]].to_sql(\"flu_cases\", engine, index=False)\n", "labels": {"reads": [{"table": "danceevents", "columns": null}], "writes": [{"table": "flu_cases", "columns": ["cid", "prereq_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitionattendance\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"healthcare\")\n", "labels": {"reads": [{"table": "exhibitionattendance", "columns": null}], "writes": [{"table": "healthcare", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.risk_score_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"thefts\")\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": null}], "writes": [{"table": "thefts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"smart_contracts_transactions\")\nwrite_to_store(df, \"conservation_programs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "smart_contracts_transactions", "columns": null}], "writes": [{"table": "conservation_programs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model traffic depends on feed\ndbt run --models traffic --vars '{\"src\":\"feed\"}'\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "traffic", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO premises SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamesessions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gamesessions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"animal_rehab\")\npersist_to_target(df, \"ads.ads_users_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": [{"table": "ads.ads_users_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vendorfabrics\").toPandas()\ndf[[\"working_year_starts\", \"inventoryid\"]].to_sql(\"arctic_research\", engine, index=False)\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": null}], "writes": [{"table": "arctic_research", "columns": ["working_year_starts", "inventoryid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM engineer_skills\"\n", "labels": {"reads": [{"table": "engineer_skills", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO news_stories SELECT level, community_members, productionrate FROM record WHERE level > 163\"\n", "labels": {"reads": [{"table": "record", "columns": ["level", "community_members", "productionrate"]}], "writes": [{"table": "news_stories", "columns": ["level", "community_members", "productionrate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM match_result\", conn)\ndf.to_sql(\"wastewater_treatment_plants\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": [{"table": "wastewater_treatment_plants", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO soccer_goals (official_native_language, ingredient_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "soccer_goals", "columns": ["official_native_language", "ingredient_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO seeds SELECT airline, num_of_factories FROM postseason WHERE airline > 485;\nEOF\n", "labels": {"reads": [{"table": "postseason", "columns": ["airline", "num_of_factories"]}], "writes": [{"table": "seeds", "columns": ["airline", "num_of_factories"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO market SELECT fare_amount, emissions, publisher FROM ods.ods_risk_score_full WHERE fare_amount > 17\")\n", "labels": {"reads": [{"table": "ods.ods_risk_score_full", "columns": ["fare_amount", "emissions", "publisher"]}], "writes": [{"table": "market", "columns": ["fare_amount", "emissions", "publisher"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM vesselfuel\"\n", "labels": {"reads": [{"table": "vesselfuel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"playersessions\").toPandas()\ndf[[\"organisation_type\", \"organic_matter\"]].to_sql(\"bioprocess.engineering_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "playersessions", "columns": null}], "writes": [{"table": "bioprocess.engineering_projects", "columns": ["organisation_type", "organic_matter"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT speed, screen_mode FROM landfill_capacity LIMIT 344;\nINSERT INTO article_views SELECT altitude, caloric_content, date_formed, worker_id FROM cargo_data WHERE altitude > 445;\nSQL\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": ["speed", "screen_mode"]}, {"table": "cargo_data", "columns": ["altitude", "caloric_content", "date_formed", "worker_id"]}], "writes": [{"table": "article_views", "columns": ["altitude", "caloric_content", "date_formed", "worker_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.dws_events_df SELECT injury_count, equipment_id, login_name, actor_id FROM regular_order_products WHERE injury_count > 228\"], check=True)\n", "labels": {"reads": [{"table": "regular_order_products", "columns": ["injury_count", "equipment_id", "login_name", "actor_id"]}], "writes": [{"table": "dws.dws_events_df", "columns": ["injury_count", "equipment_id", "login_name", "actor_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO department_stores (offense, num_pallets) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "department_stores", "columns": ["offense", "num_pallets"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT member, accreditation_type FROM contract_states LIMIT 237;\nINSERT INTO rural_feeder_roads SELECT district, personnelid, price_range, launch_date FROM steps WHERE district > 134;\nSQL\n", "labels": {"reads": [{"table": "contract_states", "columns": ["member", "accreditation_type"]}, {"table": "steps", "columns": ["district", "personnelid", "price_range", "launch_date"]}], "writes": [{"table": "rural_feeder_roads", "columns": ["district", "personnelid", "price_range", "launch_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table building --target-dir /tmp/land\n", "labels": {"reads": [{"table": "building", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT worker, phone_number FROM development_hours\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"beverages\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "development_hours", "columns": ["worker", "phone_number"]}], "writes": [{"table": "beverages", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT vaccine_type, address_line_2 FROM college LIMIT 43;\nINSERT INTO bi.bi_inventory SELECT instructor_id, company, length_feet FROM ads.orders_daily WHERE instructor_id > 336;\nSQL\n", "labels": {"reads": [{"table": "college", "columns": ["vaccine_type", "address_line_2"]}, {"table": "ads.orders_daily", "columns": ["instructor_id", "company", "length_feet"]}], "writes": [{"table": "bi.bi_inventory", "columns": ["instructor_id", "company", "length_feet"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO drought_impact SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO labor_practices SELECT * FROM legacy\ncur.execute(\"SELECT enable_dm, outcome_type FROM producersnewmexico LIMIT 234\")\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": ["enable_dm", "outcome_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO contracts SELECT attendees, lastclaimdate, museum_name FROM products_in_events WHERE attendees > 325\")\n", "labels": {"reads": [{"table": "products_in_events", "columns": ["attendees", "lastclaimdate", "museum_name"]}], "writes": [{"table": "contracts", "columns": ["attendees", "lastclaimdate", "museum_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT catalog_name, policyholderid FROM ods.campaigns_di LIMIT 54\")\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO tracklists SELECT habitat, born_state FROM clinics_sa WHERE habitat > 394\")\n", "labels": {"reads": [{"table": "ods.campaigns_di", "columns": ["catalog_name", "policyholderid"]}, {"table": "clinics_sa", "columns": ["habitat", "born_state"]}], "writes": [{"table": "tracklists", "columns": ["habitat", "born_state"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT response_time, charging_level FROM timed_locations_of_things LIMIT 378\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "timed_locations_of_things", "columns": ["response_time", "charging_level"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"communitycenters\")\nsrc.write.insertInto(\"view_unit_status\", overwrite=True)\n", "labels": {"reads": [{"table": "communitycenters", "columns": null}], "writes": [{"table": "view_unit_status", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.users_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO donation SELECT billing_city, retailer_name, last_updated FROM vr_adopters WHERE billing_city > 383\"\n", "labels": {"reads": [{"table": "vr_adopters", "columns": ["billing_city", "retailer_name", "last_updated"]}], "writes": [{"table": "donation", "columns": ["billing_city", "retailer_name", "last_updated"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"commodity_prices\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artworksales\")\n", "labels": {"reads": [{"table": "commodity_prices", "columns": null}], "writes": [{"table": "artworksales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO astronautmedicaldata (stayid, language) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "astronautmedicaldata", "columns": ["stayid", "language"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO shared_ebikes SELECT claim_status_description, negotiation_date, treatment_type, workshop_name FROM defense_contractors WHERE claim_status_description > 271\"\n", "labels": {"reads": [{"table": "defense_contractors", "columns": ["claim_status_description", "negotiation_date", "treatment_type", "workshop_name"]}], "writes": [{"table": "shared_ebikes", "columns": ["claim_status_description", "negotiation_date", "treatment_type", "workshop_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM paris_real_estate\", conn)\ndf.to_sql(\"peakhours\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "paris_real_estate", "columns": null}], "writes": [{"table": "peakhours", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO atlantic_ocean (safety_rating, delivery_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "atlantic_ocean", "columns": ["safety_rating", "delivery_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table assets --columns budget_type_description,customer_first_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "assets", "columns": ["budget_type_description", "customer_first_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT track_id, attendeename FROM impact_investments\", engine)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"characteristics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["track_id", "attendeename"]}], "writes": [{"table": "characteristics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO public.developers SELECT * FROM legacy\ncur.execute(\"SELECT cows, goal_id FROM solar_plants LIMIT 229\")\n", "labels": {"reads": [{"table": "solar_plants", "columns": ["cows", "goal_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO platformi SELECT a.trade, b.followers FROM levees a JOIN hotel_tech_adoptions b ON a.roomid = b.roomid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "levees", "columns": null}, {"table": "hotel_tech_adoptions", "columns": null}], "writes": [{"table": "platformi", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ai_papers SELECT artifact_name, spectators, approach, other_characteristic_details FROM catalogs WHERE artifact_name > 359\"\n", "labels": {"reads": [{"table": "catalogs", "columns": ["artifact_name", "spectators", "approach", "other_characteristic_details"]}], "writes": [{"table": "ai_papers", "columns": ["artifact_name", "spectators", "approach", "other_characteristic_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"government_funding\").toPandas()\ndf[[\"loan_amount\", \"call_count\"]].to_sql(\"impact_investments\", engine, index=False)\n", "labels": {"reads": [{"table": "government_funding", "columns": null}], "writes": [{"table": "impact_investments", "columns": ["loan_amount", "call_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO environmentalimpact SELECT dose, streamid FROM expensive_space_missions WHERE dose > 134\"\n", "labels": {"reads": [{"table": "expensive_space_missions", "columns": ["dose", "streamid"]}], "writes": [{"table": "environmentalimpact", "columns": ["dose", "streamid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO immunizationrates (mission_name, orderid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "immunizationrates", "columns": ["mission_name", "orderid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO customersregion SELECT courses, production_rate, area_sqkm FROM intelligence_agents WHERE courses > 294\"\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": ["courses", "production_rate", "area_sqkm"]}], "writes": [{"table": "customersregion", "columns": ["courses", "production_rate", "area_sqkm"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"satellitedata\")\npush_to_target(df, \"water_sources\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "satellitedata", "columns": null}], "writes": [{"table": "water_sources", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT time_of_day, sex FROM bi.products_daily LIMIT 357\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO accounts SELECT contract_start, billing_country, material, date_of_latest_revision FROM safety_research WHERE contract_start > 190\")\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["time_of_day", "sex"]}, {"table": "safety_research", "columns": ["contract_start", "billing_country", "material", "date_of_latest_revision"]}], "writes": [{"table": "accounts", "columns": ["contract_start", "billing_country", "material", "date_of_latest_revision"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.impressions > 211).all()\n# src table: community_policing_events\nengine.execute(\"INSERT INTO sitem SELECT * FROM community_policing_events\")\n", "labels": {"reads": [{"table": "community_policing_events", "columns": null}], "writes": [{"table": "sitem", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"middle_east_military_spending\").toPandas()\ndf[[\"artworkyear\", \"vesselid\"]].to_sql(\"shipmentinfo\", engine, index=False)\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": null}], "writes": [{"table": "shipmentinfo", "columns": ["artworkyear", "vesselid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO state_budget (sustainable, call_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "state_budget", "columns": ["sustainable", "call_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO green_energy_lending_programs SELECT a.totalamount, b.number_of_hosts FROM carbon_offset_projects a JOIN all_documents b ON a.vulnerability_score = b.vulnerability_score\"\n", "labels": {"reads": [{"table": "carbon_offset_projects", "columns": null}, {"table": "all_documents", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM communities\", conn)\ndf.to_sql(\"labor_practices\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": [{"table": "labor_practices", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"diversification_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"biotech_startups\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dams\")\n", "labels": {"reads": [{"table": "biotech_startups", "columns": null}], "writes": [{"table": "dams", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"policyholders\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"virtual_tourism\")\n", "labels": {"reads": [{"table": "policyholders", "columns": null}], "writes": [{"table": "virtual_tourism", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mentalhealthprofessional (excavationid, security_level) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mentalhealthprofessional", "columns": ["excavationid", "security_level"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO recyclingprogram SELECT safety_score, birthdate, team_id FROM viewership WHERE safety_score > 180\"], check=True)\n", "labels": {"reads": [{"table": "viewership", "columns": ["safety_score", "birthdate", "team_id"]}], "writes": [{"table": "recyclingprogram", "columns": ["safety_score", "birthdate", "team_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tourismproviders SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.international_passengers > 342).all()\n# src table: levees\nengine.execute(\"INSERT INTO totalenergyproduction SELECT * FROM levees\")\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": [{"table": "totalenergyproduction", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads.refunds (ai_algorithm_id, maintenance_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads.refunds", "columns": ["ai_algorithm_id", "maintenance_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO publication SELECT a.g_name, b.to_address FROM ads.ads_vendors_hourly a JOIN dw.exposure_di b ON a.creationyear = b.creationyear\"\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": null}, {"table": "dw.exposure_di", "columns": null}], "writes": [{"table": "publication", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bikerental SELECT inventory_id, contractor_name, reservoir_id FROM assignedto WHERE inventory_id > 102\"\n", "labels": {"reads": [{"table": "assignedto", "columns": ["inventory_id", "contractor_name", "reservoir_id"]}], "writes": [{"table": "bikerental", "columns": ["inventory_id", "contractor_name", "reservoir_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.build_date > 28).all()\n# src table: wastewater_treatment_plants\nengine.execute(\"INSERT INTO movies SELECT * FROM wastewater_treatment_plants\")\n", "labels": {"reads": [{"table": "wastewater_treatment_plants", "columns": null}], "writes": [{"table": "movies", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO inspection SELECT gdp, issue, concert_id, attendee_id FROM water_usage WHERE gdp > 197\"\n", "labels": {"reads": [{"table": "water_usage", "columns": ["gdp", "issue", "concert_id", "attendee_id"]}], "writes": [{"table": "inspection", "columns": ["gdp", "issue", "concert_id", "attendee_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO diversion_programs SELECT a.constructorid, b.detention_summary FROM safety_violations a JOIN farmer_details b ON a.paritystatus = b.paritystatus\"\n", "labels": {"reads": [{"table": "safety_violations", "columns": null}, {"table": "farmer_details", "columns": null}], "writes": [{"table": "diversion_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donationprograms SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_mammals\").toPandas()\ndf[[\"dish_id\", \"adoption_date\"]].to_sql(\"sports_events\", engine, index=False)\n", "labels": {"reads": [{"table": "marine_mammals", "columns": null}], "writes": [{"table": "sports_events", "columns": ["dish_id", "adoption_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM workout\", conn)\ndf.to_sql(\"regulatory_frameworks\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": [{"table": "regulatory_frameworks", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO trees SELECT is_unionized, booked_count FROM talent_acquisition WHERE is_unionized > 151\"], check=True)\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": ["is_unionized", "booked_count"]}], "writes": [{"table": "trees", "columns": ["is_unionized", "booked_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model circulation_history depends on public.crime_types\ndbt build --models circulation_history --vars '{\"src\":\"public.crime_types\"}'\n", "labels": {"reads": [{"table": "public.crime_types", "columns": null}], "writes": [{"table": "circulation_history", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO indigenouscommunities SELECT card_number, meal_name FROM catalog_contents WHERE card_number > 88;\nEOF\n", "labels": {"reads": [{"table": "catalog_contents", "columns": ["card_number", "meal_name"]}], "writes": [{"table": "indigenouscommunities", "columns": ["card_number", "meal_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"budget\").toPandas()\ndf[[\"total_cost\", \"account_name\"]].to_sql(\"takes\", engine, index=False)\n", "labels": {"reads": [{"table": "budget", "columns": null}], "writes": [{"table": "takes", "columns": ["total_cost", "account_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model carbon_prices_3 depends on shark_biomass\ndbt build -s carbon_prices_3 --vars '{\"src\":\"shark_biomass\"}'\n", "labels": {"reads": [{"table": "shark_biomass", "columns": null}], "writes": [{"table": "carbon_prices_3", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO lenders SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO chemical_composition SELECT transaction_date, team_id_loser, round_number FROM products_in_events WHERE transaction_date > 495\"\n", "labels": {"reads": [{"table": "products_in_events", "columns": ["transaction_date", "team_id_loser", "round_number"]}], "writes": [{"table": "chemical_composition", "columns": ["transaction_date", "team_id_loser", "round_number"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO rural.bus_trips SELECT a.document_structure_code, b.staff_name FROM school_enrollment a JOIN international_visitors b ON a.destination_name = b.destination_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "school_enrollment", "columns": null}, {"table": "international_visitors", "columns": null}], "writes": [{"table": "rural.bus_trips", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT productcategory, assessmentid FROM ads.ads_exposure_daily LIMIT 62;\nINSERT INTO music_events SELECT campaign_id, contract_id, policy_name, duration_ms FROM militarydrones WHERE campaign_id > 357;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": ["productcategory", "assessmentid"]}, {"table": "militarydrones", "columns": ["campaign_id", "contract_id", "policy_name", "duration_ms"]}], "writes": [{"table": "music_events", "columns": ["campaign_id", "contract_id", "policy_name", "duration_ms"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO marine_conservation (carrierid, jobtitle) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "marine_conservation", "columns": ["carrierid", "jobtitle"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"member\").toPandas()\ndf[[\"session_date\", \"violation_type\"]].to_sql(\"budget\", engine, index=False)\n", "labels": {"reads": [{"table": "member", "columns": null}], "writes": [{"table": "budget", "columns": ["session_date", "violation_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model sales_by_quarter depends on traditionalarts\ndbt run --models sales_by_quarter --vars 'source: traditionalarts'\n", "labels": {"reads": [{"table": "traditionalarts", "columns": null}], "writes": [{"table": "sales_by_quarter", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO event_attendance SELECT * FROM legacy\ncur.execute(\"SELECT train_number, disability FROM recycledmaterialsgarments LIMIT 239\")\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": ["train_number", "disability"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO school SELECT * FROM legacy\ncur.execute(\"SELECT co2_offset_amount, operation_type FROM wrestler LIMIT 220\")\n", "labels": {"reads": [{"table": "wrestler", "columns": ["co2_offset_amount", "operation_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table energy_prices --target-dir /tmp/land\n", "labels": {"reads": [{"table": "energy_prices", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM measurements\", conn)\ndf.to_sql(\"dwd.dwd_risk_score_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "measurements", "columns": null}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO marine_species_observations SELECT * FROM legacy\ncur.execute(\"SELECT handling_date, decision FROM miningoperations LIMIT 287\")\n", "labels": {"reads": [{"table": "miningoperations", "columns": ["handling_date", "decision"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.refunds_hourly SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO military_sales SELECT a.awards, b.kills FROM global_sales_2022 a JOIN cotton_source b ON a.bicycle_id = b.bicycle_id\"\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}, {"table": "cotton_source", "columns": null}], "writes": [{"table": "military_sales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO trends_2022 SELECT numcases, negative FROM mart_campaigns_delta WHERE numcases > 307\"\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": ["numcases", "negative"]}], "writes": [{"table": "trends_2022", "columns": ["numcases", "negative"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dw_payments\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO video_content SELECT deliverydate, news_outlet, trial_id, stayid FROM user_reactions WHERE deliverydate > 341\"\n", "labels": {"reads": [{"table": "user_reactions", "columns": ["deliverydate", "news_outlet", "trial_id", "stayid"]}], "writes": [{"table": "video_content", "columns": ["deliverydate", "news_outlet", "trial_id", "stayid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO contract_timeline SELECT stream_date, violation_type, bus_id FROM ocean_pollution WHERE stream_date > 172;\nEOF\n", "labels": {"reads": [{"table": "ocean_pollution", "columns": ["stream_date", "violation_type", "bus_id"]}], "writes": [{"table": "contract_timeline", "columns": ["stream_date", "violation_type", "bus_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT negotiation_date, date_order_placed FROM carbon_offsets\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"fans_merchandise_basketball\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["negotiation_date", "date_order_placed"]}], "writes": [{"table": "fans_merchandise_basketball", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cars --columns ethical_manufacturing,num_workers --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cars", "columns": ["ethical_manufacturing", "num_workers"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT extraction_state, hometeamid FROM safety_incidents LIMIT 173\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "safety_incidents", "columns": ["extraction_state", "hometeamid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO results SELECT community_name, prof_high_degree, address_content, participant_name FROM art_workshops WHERE community_name > 480\")\n", "labels": {"reads": [{"table": "art_workshops", "columns": ["community_name", "prof_high_degree", "address_content", "participant_name"]}], "writes": [{"table": "results", "columns": ["community_name", "prof_high_degree", "address_content", "participant_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO community_engagement SELECT productcategory, task, recipient_id FROM green_building_projects WHERE productcategory > 49\")\n", "labels": {"reads": [{"table": "green_building_projects", "columns": ["productcategory", "task", "recipient_id"]}], "writes": [{"table": "community_engagement", "columns": ["productcategory", "task", "recipient_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT ingredient_name, destroyed_by_employee_id FROM infrastructure_projects LIMIT 498\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO streams SELECT approval_date, founder_count FROM tourism_centers WHERE approval_date > 309\")\n", "labels": {"reads": [{"table": "infrastructure_projects", "columns": ["ingredient_name", "destroyed_by_employee_id"]}, {"table": "tourism_centers", "columns": ["approval_date", "founder_count"]}], "writes": [{"table": "streams", "columns": ["approval_date", "founder_count"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO architect SELECT item_price, virtual_tour_engagement_time FROM arctic_sightings WHERE item_price > 27;\nEOF\n", "labels": {"reads": [{"table": "arctic_sightings", "columns": ["item_price", "virtual_tour_engagement_time"]}], "writes": [{"table": "architect", "columns": ["item_price", "virtual_tour_engagement_time"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT member, temp FROM artist_demographics LIMIT 480\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "artist_demographics", "columns": ["member", "temp"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO lives_in (professional_development_programs, company_type_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "lives_in", "columns": ["professional_development_programs", "company_type_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO diseases SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_orders_di SELECT area_ha, height FROM animals WHERE area_ha > 436\"], check=True)\n", "labels": {"reads": [{"table": "animals", "columns": ["area_ha", "height"]}], "writes": [{"table": "mart_orders_di", "columns": ["area_ha", "height"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT host_id, grant_type FROM stg.stg_device_log_daily LIMIT 185\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO rehab_centers SELECT purchaseid, job_title FROM permit WHERE purchaseid > 438\")\n", "labels": {"reads": [{"table": "stg.stg_device_log_daily", "columns": ["host_id", "grant_type"]}, {"table": "permit", "columns": ["purchaseid", "job_title"]}], "writes": [{"table": "rehab_centers", "columns": ["purchaseid", "job_title"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hometown, track_id FROM defense_spending LIMIT 437\")\nimport logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO bi.bi_vendors_di SELECT label_id, customer_code FROM ocean_pollution WHERE label_id > 432\")\n", "labels": {"reads": [{"table": "defense_spending", "columns": ["hometown", "track_id"]}, {"table": "ocean_pollution", "columns": ["label_id", "customer_code"]}], "writes": [{"table": "bi.bi_vendors_di", "columns": ["label_id", "customer_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT athlete, is_ev FROM document_locations LIMIT 46;\nINSERT INTO ads.ads_campaigns_full SELECT dept_code, market_share FROM military_personnel_africa WHERE dept_code > 123;\nSQL\n", "labels": {"reads": [{"table": "document_locations", "columns": ["athlete", "is_ev"]}, {"table": "military_personnel_africa", "columns": ["dept_code", "market_share"]}], "writes": [{"table": "ads.ads_campaigns_full", "columns": ["dept_code", "market_share"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investment\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"agency_satellites\")\n", "labels": {"reads": [{"table": "investment", "columns": null}], "writes": [{"table": "agency_satellites", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO galleryc SELECT financially_capable, seating, delivery_status, retweets FROM tournaments WHERE financially_capable > 461\"\n", "labels": {"reads": [{"table": "tournaments", "columns": ["financially_capable", "seating", "delivery_status", "retweets"]}], "writes": [{"table": "galleryc", "columns": ["financially_capable", "seating", "delivery_status", "retweets"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.curator > 143).all()\n# src table: research.species\nengine.execute(\"INSERT INTO traffic_violations SELECT * FROM research.species\")\n", "labels": {"reads": [{"table": "research.species", "columns": null}], "writes": [{"table": "traffic_violations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.funding_round_id > 31).all()\n# src table: customer_size_diversity\nengine.execute(\"INSERT INTO waterconservationbudget SELECT * FROM customer_size_diversity\")\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": null}], "writes": [{"table": "waterconservationbudget", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"safetyincidents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "safetyincidents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO accommodations (chip_model, trainingdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "accommodations", "columns": ["chip_model", "trainingdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table defense_contractors --columns storm_id,impactid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "defense_contractors", "columns": ["storm_id", "impactid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO visual_arts SELECT a.dphone, b.citation_time FROM crops a JOIN shrimp_farms b ON a.menu_id = b.menu_id\"\n", "labels": {"reads": [{"table": "crops", "columns": null}, {"table": "shrimp_farms", "columns": null}], "writes": [{"table": "visual_arts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"european_healthcare\")\nexport_to_sink(df, \"oil_production\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}], "writes": [{"table": "oil_production", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO suburbs (address, school_colors) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "suburbs", "columns": ["address", "school_colors"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"student_courses\").toPandas()\ndf[[\"crane_id\", \"opid\"]].to_sql(\"shoes\", engine, index=False)\n", "labels": {"reads": [{"table": "student_courses", "columns": null}], "writes": [{"table": "shoes", "columns": ["crane_id", "opid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mart.mart_shipments_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM exhibitiondetails\", conn)\ndf.to_sql(\"fabric\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": [{"table": "fabric", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mentalhealthparityscores\"\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"new_schedules\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"budget\")\n", "labels": {"reads": [{"table": "new_schedules", "columns": null}], "writes": [{"table": "budget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model address depends on classrooms\ndbt build -s address --vars '{\"src\":\"classrooms\"}'\n", "labels": {"reads": [{"table": "classrooms", "columns": null}], "writes": [{"table": "address", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_pd\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"baseball_teams\")\n", "labels": {"reads": [{"table": "teacher_pd", "columns": null}], "writes": [{"table": "baseball_teams", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"textile_waste\")\npush_to_target(df, \"journal_committee\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "textile_waste", "columns": null}], "writes": [{"table": "journal_committee", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.work_type > 219).all()\n# src table: movies\nengine.execute(\"INSERT INTO job_postings SELECT * FROM movies\")\n", "labels": {"reads": [{"table": "movies", "columns": null}], "writes": [{"table": "job_postings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT catalog_id, staff_first_name FROM ads.ads_payments_hourly LIMIT 131\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO voting_data SELECT domestic_passengers, address_details FROM professionals WHERE domestic_passengers > 21\")\n", "labels": {"reads": [{"table": "ads.ads_payments_hourly", "columns": ["catalog_id", "staff_first_name"]}, {"table": "professionals", "columns": ["domestic_passengers", "address_details"]}], "writes": [{"table": "voting_data", "columns": ["domestic_passengers", "address_details"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table gymnast --target-dir /tmp/land\n", "labels": {"reads": [{"table": "gymnast", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads_vendors_hourly SELECT courses, follows_ethical_practices FROM ods.ods_member_point_df WHERE courses > 271\"\n", "labels": {"reads": [{"table": "ods.ods_member_point_df", "columns": ["courses", "follows_ethical_practices"]}], "writes": [{"table": "ads_vendors_hourly", "columns": ["courses", "follows_ethical_practices"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT no_of_customers, budget_amount FROM donationprograms LIMIT 492;\nINSERT INTO districts SELECT debate_id, platform_id FROM mart.mart_payments_delta WHERE debate_id > 240;\nSQL\n", "labels": {"reads": [{"table": "donationprograms", "columns": ["no_of_customers", "budget_amount"]}, {"table": "mart.mart_payments_delta", "columns": ["debate_id", "platform_id"]}], "writes": [{"table": "districts", "columns": ["debate_id", "platform_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT area_sqkm, class_president_vote FROM hospitallocations\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"ticket_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hospitallocations", "columns": ["area_sqkm", "class_president_vote"]}], "writes": [{"table": "ticket_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dws.inventory_df SELECT community_id, subject_id FROM production_sites WHERE community_id > 15\"\n", "labels": {"reads": [{"table": "production_sites", "columns": ["community_id", "subject_id"]}], "writes": [{"table": "dws.inventory_df", "columns": ["community_id", "subject_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_campaigns_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"militaryequipment\")\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": null}], "writes": [{"table": "militaryequipment", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO policyholders SELECT visitor_count, amount_funded, membership, artistid FROM employment WHERE visitor_count > 9\"\n", "labels": {"reads": [{"table": "employment", "columns": ["visitor_count", "amount_funded", "membership", "artistid"]}], "writes": [{"table": "policyholders", "columns": ["visitor_count", "amount_funded", "membership", "artistid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO contract_timeline SELECT registration_id, join_date FROM retailers WHERE registration_id > 285\"\n", "labels": {"reads": [{"table": "retailers", "columns": ["registration_id", "join_date"]}], "writes": [{"table": "contract_timeline", "columns": ["registration_id", "join_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dateundergoes, daily_sales FROM initiative_types\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\ndf.to_sql(\"preferences\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "initiative_types", "columns": ["dateundergoes", "daily_sales"]}], "writes": [{"table": "preferences", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table animal_population --target-dir /tmp/land\n", "labels": {"reads": [{"table": "animal_population", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"manufacturermaterials\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.mart_vendors\")\n", "labels": {"reads": [{"table": "manufacturermaterials", "columns": null}], "writes": [{"table": "mart.mart_vendors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO healthequitymetrics (algorithm, patentexpirationdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "healthequitymetrics", "columns": ["algorithm", "patentexpirationdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.claimdate > 144).all()\n# src table: battery_projects\nengine.execute(\"INSERT INTO circular_economy_initiatives SELECT * FROM battery_projects\")\n", "labels": {"reads": [{"table": "battery_projects", "columns": null}], "writes": [{"table": "circular_economy_initiatives", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO ai_safety_papers2 SELECT nickname, asset_make, onscholarship FROM user_likes WHERE nickname > 316\")\n", "labels": {"reads": [{"table": "user_likes", "columns": ["nickname", "asset_make", "onscholarship"]}], "writes": [{"table": "ai_safety_papers2", "columns": ["nickname", "asset_make", "onscholarship"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mental_health_parity_violations\")\nsrc.write.insertInto(\"mammals\", overwrite=True)\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}], "writes": [{"table": "mammals", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO euro_champs_track_field SELECT group_id, resource_id FROM restorative_justice_sentences WHERE group_id > 15\"\n", "labels": {"reads": [{"table": "restorative_justice_sentences", "columns": ["group_id", "resource_id"]}], "writes": [{"table": "euro_champs_track_field", "columns": ["group_id", "resource_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table fieldd_info --target-dir /tmp/land\n", "labels": {"reads": [{"table": "fieldd_info", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO facility_production SELECT enroll_grade, school_name, dockingdate FROM guests WHERE enroll_grade > 465\"\n", "labels": {"reads": [{"table": "guests", "columns": ["enroll_grade", "school_name", "dockingdate"]}], "writes": [{"table": "facility_production", "columns": ["enroll_grade", "school_name", "dockingdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT model, membership FROM hotel_chains\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"rd_expenditure\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hotel_chains", "columns": ["model", "membership"]}], "writes": [{"table": "rd_expenditure", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO fair_wages SELECT mission_name, complaint_status_code, cmi_details FROM languagesatrisk WHERE mission_name > 14\")\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": ["mission_name", "complaint_status_code", "cmi_details"]}], "writes": [{"table": "fair_wages", "columns": ["mission_name", "complaint_status_code", "cmi_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"park\")\nsave_to_target(df, \"platform\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "park", "columns": null}], "writes": [{"table": "platform", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO consumer SELECT inventory_id, institution_id, founder_ethnicity, invoice_date FROM climber WHERE inventory_id > 14\"\n", "labels": {"reads": [{"table": "climber", "columns": ["inventory_id", "institution_id", "founder_ethnicity", "invoice_date"]}], "writes": [{"table": "consumer", "columns": ["inventory_id", "institution_id", "founder_ethnicity", "invoice_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT provider_parity_score, fertilizer_id FROM budgets LIMIT 110\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "budgets", "columns": ["provider_parity_score", "fertilizer_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO product_details SELECT campaign_id, last_name, sodium, is_safe FROM smartcities WHERE campaign_id > 389\"\n", "labels": {"reads": [{"table": "smartcities", "columns": ["campaign_id", "last_name", "sodium", "is_safe"]}], "writes": [{"table": "product_details", "columns": ["campaign_id", "last_name", "sodium", "is_safe"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO city_waste_generation SELECT a.market_share, b.asset_type FROM ref_document_status a JOIN environmental_impact b ON a.disability = b.disability\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ref_document_status", "columns": null}, {"table": "environmental_impact", "columns": null}], "writes": [{"table": "city_waste_generation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO league SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO student_program_mapping SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vessel_registry\", conn)\ndf.to_sql(\"galleries\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vessel_registry", "columns": null}], "writes": [{"table": "galleries", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO stats SELECT visitor_country, votes, taxi_model FROM department_stores WHERE visitor_country > 298\"\n", "labels": {"reads": [{"table": "department_stores", "columns": ["visitor_country", "votes", "taxi_model"]}], "writes": [{"table": "stats", "columns": ["visitor_country", "votes", "taxi_model"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table spacecrafts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "spacecrafts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.device_name > 470).all()\n# src table: exhibition\nengine.execute(\"INSERT INTO recyclingrates SELECT * FROM exhibition\")\n", "labels": {"reads": [{"table": "exhibition", "columns": null}], "writes": [{"table": "recyclingrates", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO conservation_initiatives SELECT a.causeid, b.email FROM esportsevents a JOIN county b ON a.sustainability_id = b.sustainability_id\"\n", "labels": {"reads": [{"table": "esportsevents", "columns": null}, {"table": "county", "columns": null}], "writes": [{"table": "conservation_initiatives", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT role_description, bedtype FROM excavation LIMIT 382;\nINSERT INTO dw_member_point_full SELECT venue, attorney_last_name, functional_area_description, sentence_length FROM water_sources WHERE venue > 489;\nSQL\n", "labels": {"reads": [{"table": "excavation", "columns": ["role_description", "bedtype"]}, {"table": "water_sources", "columns": ["venue", "attorney_last_name", "functional_area_description", "sentence_length"]}], "writes": [{"table": "dw_member_point_full", "columns": ["venue", "attorney_last_name", "functional_area_description", "sentence_length"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO co_ownership SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"vendors\")\nsave_to_sink(df, \"ratings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vendors", "columns": null}], "writes": [{"table": "ratings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cuisine\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cuisine", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sd_id, supplier_company_id FROM tracklists LIMIT 41\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "tracklists", "columns": ["sd_id", "supplier_company_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO accessible_tech_categories SELECT a.artifactname, b.catalog_level_number FROM cb_agreements a JOIN healthcare b ON a.home_games = b.home_games\"\n", "labels": {"reads": [{"table": "cb_agreements", "columns": null}, {"table": "healthcare", "columns": null}], "writes": [{"table": "accessible_tech_categories", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO caribbeansea SELECT a.launch_company, b.destruction_authorised_by_employee_id FROM technician a JOIN courts b ON a.u_id = b.u_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "technician", "columns": null}, {"table": "courts", "columns": null}], "writes": [{"table": "caribbeansea", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT productionrate, event_type FROM maintenance_schedule LIMIT 286;\nINSERT INTO sites SELECT number_cities, fabrictype, team_id, date_of_latest_revision FROM crime_reports WHERE number_cities > 498;\nSQL\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": ["productionrate", "event_type"]}, {"table": "crime_reports", "columns": ["number_cities", "fabrictype", "team_id", "date_of_latest_revision"]}], "writes": [{"table": "sites", "columns": ["number_cities", "fabrictype", "team_id", "date_of_latest_revision"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO song SELECT a.donator_name, b.asset_model FROM tree_types a JOIN travel_advisory b ON a.providerid = b.providerid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tree_types", "columns": null}, {"table": "travel_advisory", "columns": null}], "writes": [{"table": "song", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"daily_revenue\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"donations2022\")\n", "labels": {"reads": [{"table": "daily_revenue", "columns": null}], "writes": [{"table": "donations2022", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shariah_financing SELECT price_range, governor, document_status_code, max_wind_speed_mph FROM wine WHERE price_range > 455\"\n", "labels": {"reads": [{"table": "wine", "columns": ["price_range", "governor", "document_status_code", "max_wind_speed_mph"]}], "writes": [{"table": "shariah_financing", "columns": ["price_range", "governor", "document_status_code", "max_wind_speed_mph"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO shariah_compliant_products (date_of_notes, taskid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "shariah_compliant_products", "columns": ["date_of_notes", "taskid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vr_adopters\", conn)\ndf.to_sql(\"artist_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": [{"table": "artist_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"construction_labor_stats\")\nsrc.write.insertInto(\"space_telescopes\", overwrite=True)\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": null}], "writes": [{"table": "space_telescopes", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO carbon_pricing SELECT training_id, park, stu_gpa FROM ads_users_hourly WHERE training_id > 156\"\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": ["training_id", "park", "stu_gpa"]}], "writes": [{"table": "carbon_pricing", "columns": ["training_id", "park", "stu_gpa"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO therapy_sessions SELECT date_of_ceremony, rank, hiv, initiativeid FROM waterconsumptionbyoperation WHERE date_of_ceremony > 384;\nEOF\n", "labels": {"reads": [{"table": "waterconsumptionbyoperation", "columns": ["date_of_ceremony", "rank", "hiv", "initiativeid"]}], "writes": [{"table": "therapy_sessions", "columns": ["date_of_ceremony", "rank", "hiv", "initiativeid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO ods_risk_score_delta SELECT mission_name, organisation_type_description FROM mappinglengths WHERE mission_name > 16\"\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["mission_name", "organisation_type_description"]}], "writes": [{"table": "ods_risk_score_delta", "columns": ["mission_name", "organisation_type_description"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart_refunds SELECT research_name, recipe_id, participation_id FROM ai_safety WHERE research_name > 163\"\n", "labels": {"reads": [{"table": "ai_safety", "columns": ["research_name", "recipe_id", "participation_id"]}], "writes": [{"table": "mart_refunds", "columns": ["research_name", "recipe_id", "participation_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT built_year, currency FROM cmi_cross_references LIMIT 474;\nINSERT INTO show SELECT playtime, primary_conference, annual_revenue FROM renewable_energy_investments WHERE playtime > 479;\nSQL\n", "labels": {"reads": [{"table": "cmi_cross_references", "columns": ["built_year", "currency"]}, {"table": "renewable_energy_investments", "columns": ["playtime", "primary_conference", "annual_revenue"]}], "writes": [{"table": "show", "columns": ["playtime", "primary_conference", "annual_revenue"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"policyanalysis\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tourism\")\n", "labels": {"reads": [{"table": "policyanalysis", "columns": null}], "writes": [{"table": "tourism", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO public_works_projects SELECT * FROM legacy\ncur.execute(\"SELECT game_count, founded FROM inspectiondata LIMIT 118\")\n", "labels": {"reads": [{"table": "inspectiondata", "columns": ["game_count", "founded"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT spacecraft_model, advocate_name FROM innovation_grants\", engine)\nimport logging\ndf.to_sql(\"ads.campaigns_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "innovation_grants", "columns": ["spacecraft_model", "advocate_name"]}], "writes": [{"table": "ads.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"workouts\")\nsrc.write.insertInto(\"bi.coupon_use\", overwrite=True)\n", "labels": {"reads": [{"table": "workouts", "columns": null}], "writes": [{"table": "bi.coupon_use", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO southchinasea.wells SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artwork (date_of_transaction, rank_in_round) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artwork", "columns": ["date_of_transaction", "rank_in_round"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.avg_depth > 81).all()\n# src table: stg.stg_exposure_daily\nengine.execute(\"INSERT INTO ods.coupon_use SELECT * FROM stg.stg_exposure_daily\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": null}], "writes": [{"table": "ods.coupon_use", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO funding_rounds SELECT contractorid, meal_id, accessibility FROM equipmentsales WHERE contractorid > 70\"\n", "labels": {"reads": [{"table": "equipmentsales", "columns": ["contractorid", "meal_id", "accessibility"]}], "writes": [{"table": "funding_rounds", "columns": ["contractorid", "meal_id", "accessibility"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"subway\").toPandas()\ndf[[\"attribute_data_type\", \"last_checkup_date\"]].to_sql(\"user_workouts_march\", engine, index=False)\n", "labels": {"reads": [{"table": "subway", "columns": null}], "writes": [{"table": "user_workouts_march", "columns": ["attribute_data_type", "last_checkup_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO runs SELECT dispensary_name, sustainabilityid, injury_count, avg_usage FROM waste_production WHERE dispensary_name > 208\"\n", "labels": {"reads": [{"table": "waste_production", "columns": ["dispensary_name", "sustainabilityid", "injury_count", "avg_usage"]}], "writes": [{"table": "runs", "columns": ["dispensary_name", "sustainabilityid", "injury_count", "avg_usage"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT resource_id, trip_type FROM fairtradecertification LIMIT 52\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO harvest_permits SELECT shop_details, inclusive_housing_policy FROM biosensors.readings WHERE shop_details > 465\")\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": ["resource_id", "trip_type"]}, {"table": "biosensors.readings", "columns": ["shop_details", "inclusive_housing_policy"]}], "writes": [{"table": "harvest_permits", "columns": ["shop_details", "inclusive_housing_policy"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO royal_family SELECT providerid, issue_date FROM regions WHERE providerid > 327\"\n", "labels": {"reads": [{"table": "regions", "columns": ["providerid", "issue_date"]}], "writes": [{"table": "royal_family", "columns": ["providerid", "issue_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tourism_centers SELECT sales_amount, call_time, chemicalid FROM login_attempts WHERE sales_amount > 170\"\n", "labels": {"reads": [{"table": "login_attempts", "columns": ["sales_amount", "call_time", "chemicalid"]}], "writes": [{"table": "tourism_centers", "columns": ["sales_amount", "call_time", "chemicalid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table green_projects --columns haslegalprecedent,safety_rating --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "green_projects", "columns": ["haslegalprecedent", "safety_rating"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO rooms SELECT dept_store_chain_id, game_name, num_developments FROM product_reviews WHERE dept_store_chain_id > 489\"\n", "labels": {"reads": [{"table": "product_reviews", "columns": ["dept_store_chain_id", "game_name", "num_developments"]}], "writes": [{"table": "rooms", "columns": ["dept_store_chain_id", "game_name", "num_developments"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_video_view\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"device_accessibility\")\n", "labels": {"reads": [{"table": "user_video_view", "columns": null}], "writes": [{"table": "device_accessibility", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO people SELECT mealid, document_status_description, release_date, investmenttype FROM cargo_handling WHERE mealid > 75\"], check=True)\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["mealid", "document_status_description", "release_date", "investmenttype"]}], "writes": [{"table": "people", "columns": ["mealid", "document_status_description", "release_date", "investmenttype"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO government.region SELECT a.emp_num, b.detention_type_code FROM birds a JOIN all_programs b ON a.clinic_type = b.clinic_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "birds", "columns": null}, {"table": "all_programs", "columns": null}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO manufacturing_processes SELECT species, wrestler_id, movie_id FROM dws.risk_score_daily WHERE species > 369\"\n", "labels": {"reads": [{"table": "dws.risk_score_daily", "columns": ["species", "wrestler_id", "movie_id"]}], "writes": [{"table": "manufacturing_processes", "columns": ["species", "wrestler_id", "movie_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"schools\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "schools", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO shariahfinance SELECT thefttypeid, fiscal_year, medical_risk, ship_id FROM criminal_cases WHERE thefttypeid > 322\"\n", "labels": {"reads": [{"table": "criminal_cases", "columns": ["thefttypeid", "fiscal_year", "medical_risk", "ship_id"]}], "writes": [{"table": "shariahfinance", "columns": ["thefttypeid", "fiscal_year", "medical_risk", "ship_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.building_type > 205).all()\n# src table: city_labor_cost\nengine.execute(\"INSERT INTO bike_share SELECT * FROM city_labor_cost\")\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}], "writes": [{"table": "bike_share", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO atlantic_plate (taxi_model, archaeologistid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "atlantic_plate", "columns": ["taxi_model", "archaeologistid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO regional_archaeologists SELECT num_libraries, home_games FROM family_cases WHERE num_libraries > 139\"\n", "labels": {"reads": [{"table": "family_cases", "columns": ["num_libraries", "home_games"]}], "writes": [{"table": "regional_archaeologists", "columns": ["num_libraries", "home_games"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO swimmer SELECT lettergrade, length FROM mart_campaigns_delta WHERE lettergrade > 96\"\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": ["lettergrade", "length"]}], "writes": [{"table": "swimmer", "columns": ["lettergrade", "length"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO market_access SELECT source, artist_id FROM audience_demographics WHERE source > 223\"\n", "labels": {"reads": [{"table": "audience_demographics", "columns": ["source", "artist_id"]}], "writes": [{"table": "market_access", "columns": ["source", "artist_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT male_id, date_moved_in FROM ads.orders LIMIT 16\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "ads.orders", "columns": ["male_id", "date_moved_in"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO daily_articles_by_category (cause, wage) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "daily_articles_by_category", "columns": ["cause", "wage"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"landfillcapacitybycountry\")\nsave_to_target(df, \"scan_dates\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": null}], "writes": [{"table": "scan_dates", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hires\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"recalls\")\n", "labels": {"reads": [{"table": "hires", "columns": null}], "writes": [{"table": "recalls", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gender\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gender", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO country_waste_generation SELECT shipment_id, campaign_id, playtime, workers FROM ads.orders_daily WHERE shipment_id > 13\"\n", "labels": {"reads": [{"table": "ads.orders_daily", "columns": ["shipment_id", "campaign_id", "playtime", "workers"]}], "writes": [{"table": "country_waste_generation", "columns": ["shipment_id", "campaign_id", "playtime", "workers"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dwd.coupon_use_full\")\nsrc.write.insertInto(\"city_department\", overwrite=True)\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": null}], "writes": [{"table": "city_department", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_risk_score_delta\").toPandas()\ndf[[\"physician\", \"representative_name\"]].to_sql(\"startup_founders\", engine, index=False)\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": null}], "writes": [{"table": "startup_founders", "columns": ["physician", "representative_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_orders_daily\", conn)\ndf.to_sql(\"marathons\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_orders_daily", "columns": null}], "writes": [{"table": "marathons", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO date SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sustainablebrands\"\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table foodsafetyrecords --columns recycler_id,part_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "foodsafetyrecords", "columns": ["recycler_id", "part_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cause_name > 290).all()\n# src table: community_development_projects\nengine.execute(\"INSERT INTO stg.cart_item_full SELECT * FROM community_development_projects\")\n", "labels": {"reads": [{"table": "community_development_projects", "columns": null}], "writes": [{"table": "stg.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO navalequipmentmaintenance SELECT users_engaged, farmid, co2_emissions FROM exhibition_artworks WHERE users_engaged > 94\"\n", "labels": {"reads": [{"table": "exhibition_artworks", "columns": ["users_engaged", "farmid", "co2_emissions"]}], "writes": [{"table": "navalequipmentmaintenance", "columns": ["users_engaged", "farmid", "co2_emissions"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM states\", conn)\ndf.to_sql(\"ads.ads_payments_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "states", "columns": null}], "writes": [{"table": "ads.ads_payments_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT artist_gender, trial_success_rate FROM user_likes LIMIT 141\")\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO inclusion_efforts SELECT player, service_type, entryid, volunteer_date FROM gymnast WHERE player > 497\")\n", "labels": {"reads": [{"table": "user_likes", "columns": ["artist_gender", "trial_success_rate"]}, {"table": "gymnast", "columns": ["player", "service_type", "entryid", "volunteer_date"]}], "writes": [{"table": "inclusion_efforts", "columns": ["player", "service_type", "entryid", "volunteer_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"school_details\").toPandas()\ndf[[\"species\", \"clubid\"]].to_sql(\"buildingpermits\", engine, index=False)\n", "labels": {"reads": [{"table": "school_details", "columns": null}], "writes": [{"table": "buildingpermits", "columns": ["species", "clubid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO home_game SELECT a.supplier_name, b.menu_category FROM product_catalog a JOIN healthcare_facilities b ON a.trial_year = b.trial_year\"\n", "labels": {"reads": [{"table": "product_catalog", "columns": null}, {"table": "healthcare_facilities", "columns": null}], "writes": [{"table": "home_game", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecurity_incidents\").toPandas()\ndf[[\"donor_category\", \"productname\"]].to_sql(\"rural_clinics\", engine, index=False)\n", "labels": {"reads": [{"table": "cybersecurity_incidents", "columns": null}], "writes": [{"table": "rural_clinics", "columns": ["donor_category", "productname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM player\"\n", "labels": {"reads": [{"table": "player", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO train_lines SELECT countryname, dormid, product_color FROM fabrics WHERE countryname > 262\"\n", "labels": {"reads": [{"table": "fabrics", "columns": ["countryname", "dormid", "product_color"]}], "writes": [{"table": "train_lines", "columns": ["countryname", "dormid", "product_color"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM social_impact_bonds\", conn)\ndf.to_sql(\"legalaidrequests\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "social_impact_bonds", "columns": null}], "writes": [{"table": "legalaidrequests", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO salary SELECT safety_score, frameworkcountry, number_cities, permit_id FROM communitycourts WHERE safety_score > 359\"\n", "labels": {"reads": [{"table": "communitycourts", "columns": ["safety_score", "frameworkcountry", "number_cities", "permit_id"]}], "writes": [{"table": "salary", "columns": ["safety_score", "frameworkcountry", "number_cities", "permit_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model weekly_weather depends on emergencies\ndbt build -s weekly_weather --vars 'source: emergencies'\n", "labels": {"reads": [{"table": "emergencies", "columns": null}], "writes": [{"table": "weekly_weather", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO rental (inventor_name, cuisine_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "rental", "columns": ["inventor_name", "cuisine_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO teams SELECT transaction_type_description, min_salary FROM reverselogisticstransactions WHERE transaction_type_description > 378\"\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": ["transaction_type_description", "min_salary"]}], "writes": [{"table": "teams", "columns": ["transaction_type_description", "min_salary"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.vessel_name > 104).all()\n# src table: clothingsales\nengine.execute(\"INSERT INTO genetic_research SELECT * FROM clothingsales\")\n", "labels": {"reads": [{"table": "clothingsales", "columns": null}], "writes": [{"table": "genetic_research", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model riskassessments depends on departments\ndbt build --models riskassessments --vars 'source: departments'\n", "labels": {"reads": [{"table": "departments", "columns": null}], "writes": [{"table": "riskassessments", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO all_documents SELECT a.condition, b.updated_at FROM ads.refunds_delta a JOIN educationprograms b ON a.thefttypeid = b.thefttypeid\"\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": null}, {"table": "educationprograms", "columns": null}], "writes": [{"table": "all_documents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO flu_cases SELECT discount, garment_type, driller, airport FROM customer WHERE discount > 405\"\n", "labels": {"reads": [{"table": "customer", "columns": ["discount", "garment_type", "driller", "airport"]}], "writes": [{"table": "flu_cases", "columns": ["discount", "garment_type", "driller", "airport"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO immunizationrates SELECT a.instructor, b.production_qty FROM screen_mode a JOIN carbonoffsetinitiatives b ON a.time_of_purchase = b.time_of_purchase\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "screen_mode", "columns": null}, {"table": "carbonoffsetinitiatives", "columns": null}], "writes": [{"table": "immunizationrates", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table transportation --target-dir /tmp/land\n", "labels": {"reads": [{"table": "transportation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"teaches\")\npersist_to_sink(df, \"crime_reports\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "teaches", "columns": null}], "writes": [{"table": "crime_reports", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO ref_budget_codes SELECT license_plate, target_name, amount_donated FROM labor_practices WHERE license_plate > 178\"\n", "labels": {"reads": [{"table": "labor_practices", "columns": ["license_plate", "target_name", "amount_donated"]}], "writes": [{"table": "ref_budget_codes", "columns": ["license_plate", "target_name", "amount_donated"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"item_prices\")\nwrite_to_output(df, \"feedback\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "item_prices", "columns": null}], "writes": [{"table": "feedback", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table government.region --target-dir /tmp/land\n", "labels": {"reads": [{"table": "government.region", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table systems --target-dir /tmp/land\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT grant_date, transit_passengers FROM state_budget\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"flight_emissions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "state_budget", "columns": ["grant_date", "transit_passengers"]}], "writes": [{"table": "flight_emissions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO paintings SELECT feature_details, paperid, ethnicity FROM ads.risk_score WHERE feature_details > 241\"\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["feature_details", "paperid", "ethnicity"]}], "writes": [{"table": "paintings", "columns": ["feature_details", "paperid", "ethnicity"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"efforts\").toPandas()\ndf[[\"screening\", \"actor_name\"]].to_sql(\"virtual_tour_stats\", engine, index=False)\n", "labels": {"reads": [{"table": "efforts", "columns": null}], "writes": [{"table": "virtual_tour_stats", "columns": ["screening", "actor_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO clothingitems SELECT a.distance, b.market_value_billion FROM innovation_metrics a JOIN locations b ON a.stu_gpa = b.stu_gpa\"\n", "labels": {"reads": [{"table": "innovation_metrics", "columns": null}, {"table": "locations", "columns": null}], "writes": [{"table": "clothingitems", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO marine_mammals SELECT algorithmic_fairness_score, assessmentname, mineral, audienceid FROM dwd.vendors WHERE algorithmic_fairness_score > 116\"\n", "labels": {"reads": [{"table": "dwd.vendors", "columns": ["algorithmic_fairness_score", "assessmentname", "mineral", "audienceid"]}], "writes": [{"table": "marine_mammals", "columns": ["algorithmic_fairness_score", "assessmentname", "mineral", "audienceid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"autonomous_testing\")\nsrc.write.insertInto(\"organic_cosmetics\", overwrite=True)\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": null}], "writes": [{"table": "organic_cosmetics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemicals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "chemicals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.coupon_use_full\", conn)\ndf.to_sql(\"culturalcompetency\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": null}], "writes": [{"table": "culturalcompetency", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO player_f SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_leaders\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"attack_outcomes\")\n", "labels": {"reads": [{"table": "community_leaders", "columns": null}], "writes": [{"table": "attack_outcomes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT schedule_date, playlist_id FROM dw.dw_events_di LIMIT 99;\nINSERT INTO sales SELECT insurancetype, project_name, prof_office FROM mart.campaigns_full WHERE insurancetype > 323;\nSQL\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": ["schedule_date", "playlist_id"]}, {"table": "mart.campaigns_full", "columns": ["insurancetype", "project_name", "prof_office"]}], "writes": [{"table": "sales", "columns": ["insurancetype", "project_name", "prof_office"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM recycling_stats\"\n", "labels": {"reads": [{"table": "recycling_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"cargos\")\nsrc.write.insertInto(\"sponsorship_donations\", overwrite=True)\n", "labels": {"reads": [{"table": "cargos", "columns": null}], "writes": [{"table": "sponsorship_donations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO wellbeing_programs SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table savings_programs --columns accident_date,major --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "savings_programs", "columns": ["accident_date", "major"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"culturalcompetencytrainings\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"gold\")\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": null}], "writes": [{"table": "gold", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads_users_hourly SELECT payment_method_code, material_id, hireid FROM bridge WHERE payment_method_code > 251\"\n", "labels": {"reads": [{"table": "bridge", "columns": ["payment_method_code", "material_id", "hireid"]}], "writes": [{"table": "ads_users_hourly", "columns": ["payment_method_code", "material_id", "hireid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainableprojects SELECT cargo_type, event_type_id, carrierid FROM livestock WHERE cargo_type > 96\"\n", "labels": {"reads": [{"table": "livestock", "columns": ["cargo_type", "event_type_id", "carrierid"]}], "writes": [{"table": "sustainableprojects", "columns": ["cargo_type", "event_type_id", "carrierid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO wedding SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"station_crime_rates\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "station_crime_rates", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO initiatives_3 SELECT * FROM legacy\ncur.execute(\"SELECT diet, defense_contractor_id FROM ocean_acidification_antarctic LIMIT 191\")\n", "labels": {"reads": [{"table": "ocean_acidification_antarctic", "columns": ["diet", "defense_contractor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"new_schedules\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"animals\")\n", "labels": {"reads": [{"table": "new_schedules", "columns": null}], "writes": [{"table": "animals", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"program\").toPandas()\ndf[[\"field_id\", \"courtid\"]].to_sql(\"ads.member_point\", engine, index=False)\n", "labels": {"reads": [{"table": "program", "columns": null}], "writes": [{"table": "ads.member_point", "columns": ["field_id", "courtid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO track (job, hourid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "track", "columns": ["job", "hourid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attribute_id, overall_rating FROM journal_committee\", engine)\nimport logging\ndf.to_sql(\"jupiter_missions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "journal_committee", "columns": ["attribute_id", "overall_rating"]}], "writes": [{"table": "jupiter_missions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO home_game SELECT system_id, eventtype, community_center_id FROM service_budget WHERE system_id > 419;\nEOF\n", "labels": {"reads": [{"table": "service_budget", "columns": ["system_id", "eventtype", "community_center_id"]}], "writes": [{"table": "home_game", "columns": ["system_id", "eventtype", "community_center_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO office_locations SELECT pollution_id, party_phone, enrollment, subscriber_type FROM sponsor_trials WHERE pollution_id > 335\"\n", "labels": {"reads": [{"table": "sponsor_trials", "columns": ["pollution_id", "party_phone", "enrollment", "subscriber_type"]}], "writes": [{"table": "office_locations", "columns": ["pollution_id", "party_phone", "enrollment", "subscriber_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"university\").toPandas()\ndf[[\"prereq_id\", \"review_text\"]].to_sql(\"stg.users\", engine, index=False)\n", "labels": {"reads": [{"table": "university", "columns": null}], "writes": [{"table": "stg.users", "columns": ["prereq_id", "review_text"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dno, item_sold FROM circularsupplychain\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"public.crime_types\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "circularsupplychain", "columns": ["dno", "item_sold"]}], "writes": [{"table": "public.crime_types", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO countries (num_solo_exhibitions, provider_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "countries", "columns": ["num_solo_exhibitions", "provider_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO habitat SELECT partner_id, surface_area FROM networkdevices WHERE partner_id > 352\"], check=True)\n", "labels": {"reads": [{"table": "networkdevices", "columns": ["partner_id", "surface_area"]}], "writes": [{"table": "habitat", "columns": ["partner_id", "surface_area"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"employment\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"trains\")\n", "labels": {"reads": [{"table": "employment", "columns": null}], "writes": [{"table": "trains", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"accelerator_compatible_browser\").toPandas()\ndf[[\"num_employees\", \"quality\"]].to_sql(\"papers\", engine, index=False)\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": null}], "writes": [{"table": "papers", "columns": ["num_employees", "quality"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO drama_workshop_groups SELECT amount_outstanding, workshop_name FROM communitydevelopment WHERE amount_outstanding > 329\"], check=True)\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": ["amount_outstanding", "workshop_name"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["amount_outstanding", "workshop_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO culturalcompetency (drug_name, labor_hour_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "culturalcompetency", "columns": ["drug_name", "labor_hour_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"vehiclemodels\")\npush_to_target(df, \"membership_register_branch\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": null}], "writes": [{"table": "membership_register_branch", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO dispensary_sales SELECT denomination, contractor, potency FROM gamedesigndata WHERE denomination > 436\"\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": ["denomination", "contractor", "potency"]}], "writes": [{"table": "dispensary_sales", "columns": ["denomination", "contractor", "potency"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM recyclers\"\n", "labels": {"reads": [{"table": "recyclers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT district_id, is_recycled FROM brandrevenue LIMIT 479;\nINSERT INTO pollution_control_initiatives SELECT head, outcome_type, garment FROM legalaidrequests WHERE head > 296;\nSQL\n", "labels": {"reads": [{"table": "brandrevenue", "columns": ["district_id", "is_recycled"]}, {"table": "legalaidrequests", "columns": ["head", "outcome_type", "garment"]}], "writes": [{"table": "pollution_control_initiatives", "columns": ["head", "outcome_type", "garment"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table web_client_accelerator --target-dir /tmp/land\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table member_activity --target-dir /tmp/land\n", "labels": {"reads": [{"table": "member_activity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dw.dw_sessions_delta\", conn)\ndf.to_sql(\"staff_members\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": null}], "writes": [{"table": "staff_members", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO busmaintenance SELECT a.lesson_id, b.phase FROM e_scooter_trips a JOIN waste_generation b ON a.attraction_id = b.attraction_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": null}, {"table": "waste_generation", "columns": null}], "writes": [{"table": "busmaintenance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO document_sections_images (applicant, recorded_by_staff_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "document_sections_images", "columns": ["applicant", "recorded_by_staff_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO therapy_attendance SELECT mission_name, treatment_name, hashtags FROM maintenance_engineers WHERE mission_name > 21;\nEOF\n", "labels": {"reads": [{"table": "maintenance_engineers", "columns": ["mission_name", "treatment_name", "hashtags"]}], "writes": [{"table": "therapy_attendance", "columns": ["mission_name", "treatment_name", "hashtags"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hourlyrate, structure_type FROM authors\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"mine_workforce\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "authors", "columns": ["hourlyrate", "structure_type"]}], "writes": [{"table": "mine_workforce", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_innovation\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "military_innovation", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO rental SELECT zip, cust_id, albumname FROM railway WHERE zip > 469\"\n", "labels": {"reads": [{"table": "railway", "columns": ["zip", "cust_id", "albumname"]}], "writes": [{"table": "rental", "columns": ["zip", "cust_id", "albumname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT contributions, low_estimate FROM bus_fares\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"parking_fines\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bus_fares", "columns": ["contributions", "low_estimate"]}], "writes": [{"table": "parking_fines", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO biosensors.readings SELECT restaurantid, trial_name, eventname FROM attorney_billing WHERE restaurantid > 309\"\n", "labels": {"reads": [{"table": "attorney_billing", "columns": ["restaurantid", "trial_name", "eventname"]}], "writes": [{"table": "biosensors.readings", "columns": ["restaurantid", "trial_name", "eventname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO community.donations SELECT room_count, claim_status_description, club_id, budget_amount FROM container WHERE room_count > 276\"], check=True)\n", "labels": {"reads": [{"table": "container", "columns": ["room_count", "claim_status_description", "club_id", "budget_amount"]}], "writes": [{"table": "community.donations", "columns": ["room_count", "claim_status_description", "club_id", "budget_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.tot_cred > 209).all()\n# src table: dwd.dwd_orders_di\nengine.execute(\"INSERT INTO highest_scores SELECT * FROM dwd.dwd_orders_di\")\n", "labels": {"reads": [{"table": "dwd.dwd_orders_di", "columns": null}], "writes": [{"table": "highest_scores", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model races depends on community_development.transactions\ndbt build -s races --vars '{\"src\":\"community_development.transactions\"}'\n", "labels": {"reads": [{"table": "community_development.transactions", "columns": null}], "writes": [{"table": "races", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO dw.shipments_df SELECT a.order_shipping_charges, b.invoice_id FROM marketingbudget a JOIN continents b ON a.event_details = b.event_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "marketingbudget", "columns": null}, {"table": "continents", "columns": null}], "writes": [{"table": "dw.shipments_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mission_count, neighborhoodid FROM passenger_trips LIMIT 407\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO greenbuildings SELECT census_ranking, ll_id, team_id_loser FROM artworksales WHERE census_ranking > 286\")\n", "labels": {"reads": [{"table": "passenger_trips", "columns": ["mission_count", "neighborhoodid"]}, {"table": "artworksales", "columns": ["census_ranking", "ll_id", "team_id_loser"]}], "writes": [{"table": "greenbuildings", "columns": ["census_ranking", "ll_id", "team_id_loser"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"evidence_based_policies\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"tickets\")\nsrc.write.insertInto(\"mental_health_scores\", overwrite=True)\n", "labels": {"reads": [{"table": "tickets", "columns": null}], "writes": [{"table": "mental_health_scores", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mean_humidity > 391).all()\n# src table: party_events\nengine.execute(\"INSERT INTO coowners SELECT * FROM party_events\")\n", "labels": {"reads": [{"table": "party_events", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO higher_ed.students SELECT cargoid, date_formed, is_organic FROM dwd.dwd_campaigns_df WHERE cargoid > 249\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": ["cargoid", "date_formed", "is_organic"]}], "writes": [{"table": "higher_ed.students", "columns": ["cargoid", "date_formed", "is_organic"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.ads_device_log_di SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM support_tickets\", conn)\ndf.to_sql(\"participation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "support_tickets", "columns": null}], "writes": [{"table": "participation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ai_safety depends on volunteer_hours\ndbt run --models ai_safety --vars 'source: volunteer_hours'\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": [{"table": "ai_safety", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO biotech_startups SELECT spent, category_id, order_shipping_charges, connection FROM ads.orders WHERE spent > 250\"\n", "labels": {"reads": [{"table": "ads.orders", "columns": ["spent", "category_id", "order_shipping_charges", "connection"]}], "writes": [{"table": "biotech_startups", "columns": ["spent", "category_id", "order_shipping_charges", "connection"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO philadelphia_police_emergencies SELECT platform_id, volunteerhourid, truck_licence_number, grape FROM astronaut_missions WHERE platform_id > 48\"\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": ["platform_id", "volunteerhourid", "truck_licence_number", "grape"]}], "writes": [{"table": "philadelphia_police_emergencies", "columns": ["platform_id", "volunteerhourid", "truck_licence_number", "grape"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO scores SELECT * FROM legacy\ncur.execute(\"SELECT date_of_birth, pcp FROM sustainable_materials LIMIT 468\")\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": ["date_of_birth", "pcp"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"environmental_impact\")\nsave_to_target(df, \"militarybases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "environmental_impact", "columns": null}], "writes": [{"table": "militarybases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"communitypolicing\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "communitypolicing", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO continents SELECT a.medical_professional_id, b.characteristic_id FROM fish_suppliers a JOIN storage_tech b ON a.num_sessions = b.num_sessions\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": null}, {"table": "storage_tech", "columns": null}], "writes": [{"table": "continents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT feedback_id, consultations FROM restaurant_revenue LIMIT 150;\nINSERT INTO global_tournament SELECT vulnerability_score, revenueid, building_address, number_of_observations FROM ods.ods_events_daily WHERE vulnerability_score > 493;\nSQL\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": ["feedback_id", "consultations"]}, {"table": "ods.ods_events_daily", "columns": ["vulnerability_score", "revenueid", "building_address", "number_of_observations"]}], "writes": [{"table": "global_tournament", "columns": ["vulnerability_score", "revenueid", "building_address", "number_of_observations"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.document_code > 429).all()\n# src table: temperaturehistory\nengine.execute(\"INSERT INTO inclusion_efforts SELECT * FROM temperaturehistory\")\n", "labels": {"reads": [{"table": "temperaturehistory", "columns": null}], "writes": [{"table": "inclusion_efforts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table france_culture --target-dir /tmp/land\n", "labels": {"reads": [{"table": "france_culture", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_events_delta\").toPandas()\ndf[[\"goal_date\", \"worker_name\"]].to_sql(\"geothermal_power_plants\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": [{"table": "geothermal_power_plants", "columns": ["goal_date", "worker_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model al_jazeera_data depends on erc20_transactions\ndbt build --select al_jazeera_data --vars '{\"source_table\":\"erc20_transactions\"}'\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": null}], "writes": [{"table": "al_jazeera_data", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mediterranean_salinity SELECT screening_id, instrument FROM functional_areas WHERE screening_id > 110\"\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["screening_id", "instrument"]}], "writes": [{"table": "mediterranean_salinity", "columns": ["screening_id", "instrument"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sales_in_billion, individual_last_name FROM skincareinventory LIMIT 352\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "skincareinventory", "columns": ["sales_in_billion", "individual_last_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO co2_sequestration SELECT assessment_date, show_id, dissolved_oxygen, caloric_content FROM vendors WHERE assessment_date > 486\"\n", "labels": {"reads": [{"table": "vendors", "columns": ["assessment_date", "show_id", "dissolved_oxygen", "caloric_content"]}], "writes": [{"table": "co2_sequestration", "columns": ["assessment_date", "show_id", "dissolved_oxygen", "caloric_content"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO immunization SELECT city_traffic_speed, exhibition_id FROM southeast_providers WHERE city_traffic_speed > 160\"\n", "labels": {"reads": [{"table": "southeast_providers", "columns": ["city_traffic_speed", "exhibition_id"]}], "writes": [{"table": "immunization", "columns": ["city_traffic_speed", "exhibition_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO food_items SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO program_funding_2 (anomaly, invoice_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "program_funding_2", "columns": ["anomaly", "invoice_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT contactid, valuation FROM item_inventory\", engine)\nimport logging\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ocean_shipping.cargo\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "item_inventory", "columns": ["contactid", "valuation"]}], "writes": [{"table": "ocean_shipping.cargo", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO media_types SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO habitat SELECT rating, team_id_loser FROM screen_mode WHERE rating > 340;\nEOF\n", "labels": {"reads": [{"table": "screen_mode", "columns": ["rating", "team_id_loser"]}], "writes": [{"table": "habitat", "columns": ["rating", "team_id_loser"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd.vendors SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO casebilling (field_id, reaction_time) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "casebilling", "columns": ["field_id", "reaction_time"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ytterbium_supply SELECT lanes, plant, exit_type, effort_id FROM countries WHERE lanes > 465;\nEOF\n", "labels": {"reads": [{"table": "countries", "columns": ["lanes", "plant", "exit_type", "effort_id"]}], "writes": [{"table": "ytterbium_supply", "columns": ["lanes", "plant", "exit_type", "effort_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM jobs\"\n", "labels": {"reads": [{"table": "jobs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_payments_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"articles_es\")\n", "labels": {"reads": [{"table": "bi.bi_payments_delta", "columns": null}], "writes": [{"table": "articles_es", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO peacekeeping_units SELECT material, award, safety_id FROM mart.campaigns_full WHERE material > 492\"\n", "labels": {"reads": [{"table": "mart.campaigns_full", "columns": ["material", "award", "safety_id"]}], "writes": [{"table": "peacekeeping_units", "columns": ["material", "award", "safety_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO awards SELECT garmentid, building_id FROM middle_east_military_spending WHERE garmentid > 495\")\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": ["garmentid", "building_id"]}], "writes": [{"table": "awards", "columns": ["garmentid", "building_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO party_host SELECT campus, took_office FROM purchases WHERE campus > 421;\nEOF\n", "labels": {"reads": [{"table": "purchases", "columns": ["campus", "took_office"]}], "writes": [{"table": "party_host", "columns": ["campus", "took_office"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trafficviolations SELECT trial_success_rate, functional_area_description FROM status WHERE trial_success_rate > 62\"\n", "labels": {"reads": [{"table": "status", "columns": ["trial_success_rate", "functional_area_description"]}], "writes": [{"table": "trafficviolations", "columns": ["trial_success_rate", "functional_area_description"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table ads_users_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO eu_data_usage SELECT astronaut_name, train_id, operating_system, password FROM sustainable_practices WHERE astronaut_name > 285\"\n", "labels": {"reads": [{"table": "sustainable_practices", "columns": ["astronaut_name", "train_id", "operating_system", "password"]}], "writes": [{"table": "eu_data_usage", "columns": ["astronaut_name", "train_id", "operating_system", "password"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM papers\"\n", "labels": {"reads": [{"table": "papers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dw_coupon_use_daily depends on ancient_cultures\ndbt build --select dw_coupon_use_daily --vars '{\"src\":\"ancient_cultures\"}'\n", "labels": {"reads": [{"table": "ancient_cultures", "columns": null}], "writes": [{"table": "dw_coupon_use_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.order_item_status > 234).all()\n# src table: clothingitems\nengine.execute(\"INSERT INTO swimmer SELECT * FROM clothingitems\")\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}], "writes": [{"table": "swimmer", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tourdifferences SELECT moisture, session_name FROM space_exploration WHERE moisture > 321\"\n", "labels": {"reads": [{"table": "space_exploration", "columns": ["moisture", "session_name"]}], "writes": [{"table": "tourdifferences", "columns": ["moisture", "session_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"esa_missions\")\nsrc.write.insertInto(\"ngo_funding\", overwrite=True)\n", "labels": {"reads": [{"table": "esa_missions", "columns": null}], "writes": [{"table": "ngo_funding", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"supportprograms\")\nexport_to_warehouse(df, \"tickets_3\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "supportprograms", "columns": null}], "writes": [{"table": "tickets_3", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"intelligencesatellites\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.events_delta\")\n", "labels": {"reads": [{"table": "intelligencesatellites", "columns": null}], "writes": [{"table": "bi.events_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO eventlocations SELECT subject_name, precipitation, product_subcategory, review_rating FROM assets WHERE subject_name > 180\"\n", "labels": {"reads": [{"table": "assets", "columns": ["subject_name", "precipitation", "product_subcategory", "review_rating"]}], "writes": [{"table": "eventlocations", "columns": ["subject_name", "precipitation", "product_subcategory", "review_rating"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_exposure_df\").toPandas()\ndf[[\"years_played\", \"num_volunteers\"]].to_sql(\"concert_events\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": null}], "writes": [{"table": "concert_events", "columns": ["years_played", "num_volunteers"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO drug_sales SELECT school_type, detention_summary, duration_ms FROM members WHERE school_type > 69\"\n", "labels": {"reads": [{"table": "members", "columns": ["school_type", "detention_summary", "duration_ms"]}], "writes": [{"table": "drug_sales", "columns": ["school_type", "detention_summary", "duration_ms"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.shipments_full (conservation_status, scan_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.shipments_full", "columns": ["conservation_status", "scan_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT nurse, individual_last_name FROM producers\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"emergency_categories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "producers", "columns": ["nurse", "individual_last_name"]}], "writes": [{"table": "emergency_categories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO noise_pollution SELECT gamepreference, negative, company_gender, amount_paid FROM ods.ods_exposure_delta WHERE gamepreference > 299\"\n", "labels": {"reads": [{"table": "ods.ods_exposure_delta", "columns": ["gamepreference", "negative", "company_gender", "amount_paid"]}], "writes": [{"table": "noise_pollution", "columns": ["gamepreference", "negative", "company_gender", "amount_paid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table crop_temperature --columns assessment_score,account_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "crop_temperature", "columns": ["assessment_score", "account_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_tracking\").toPandas()\ndf[[\"fleet_series\", \"concertid\"]].to_sql(\"program\", engine, index=False)\n", "labels": {"reads": [{"table": "vessel_tracking", "columns": null}], "writes": [{"table": "program", "columns": ["fleet_series", "concertid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"members\").toPandas()\ndf[[\"individual_id\", \"aid_id\"]].to_sql(\"participates_in\", engine, index=False)\n", "labels": {"reads": [{"table": "members", "columns": null}], "writes": [{"table": "participates_in", "columns": ["individual_id", "aid_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO recyclingprogram SELECT city, equipment_type, document_status_code FROM safetyincidents WHERE city > 372\"\n", "labels": {"reads": [{"table": "safetyincidents", "columns": ["city", "equipment_type", "document_status_code"]}], "writes": [{"table": "recyclingprogram", "columns": ["city", "equipment_type", "document_status_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM rehab_centers\"\n", "labels": {"reads": [{"table": "rehab_centers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT well_type, fan_id FROM fans LIMIT 215\")\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO article_views SELECT event_type_id, type FROM body_builder WHERE event_type_id > 468\")\n", "labels": {"reads": [{"table": "fans", "columns": ["well_type", "fan_id"]}, {"table": "body_builder", "columns": ["event_type_id", "type"]}], "writes": [{"table": "article_views", "columns": ["event_type_id", "type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO solana_transactions SELECT grant_end_date, dphone FROM causes_insert_2 WHERE grant_end_date > 37\"\n", "labels": {"reads": [{"table": "causes_insert_2", "columns": ["grant_end_date", "dphone"]}], "writes": [{"table": "solana_transactions", "columns": ["grant_end_date", "dphone"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO thefttypes SELECT posted_at, start_therapy, goal_id FROM busmaintenance WHERE posted_at > 223\"\n", "labels": {"reads": [{"table": "busmaintenance", "columns": ["posted_at", "start_therapy", "goal_id"]}], "writes": [{"table": "thefttypes", "columns": ["posted_at", "start_therapy", "goal_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gross_worldwide, siteid FROM water_sources LIMIT 101\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "water_sources", "columns": ["gross_worldwide", "siteid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT num_solo_exhibitions, reo_type FROM apartments LIMIT 21;\nINSERT INTO show SELECT itemname, billingcountry, receipt_date, sustainability_id FROM market WHERE itemname > 264;\nSQL\n", "labels": {"reads": [{"table": "apartments", "columns": ["num_solo_exhibitions", "reo_type"]}, {"table": "market", "columns": ["itemname", "billingcountry", "receipt_date", "sustainability_id"]}], "writes": [{"table": "show", "columns": ["itemname", "billingcountry", "receipt_date", "sustainability_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy_exam\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.dwd_exposure_df\")\n", "labels": {"reads": [{"table": "open_pedagogy_exam", "columns": null}], "writes": [{"table": "dwd.dwd_exposure_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT song, channel_code FROM satellite_deployment LIMIT 385\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "satellite_deployment", "columns": ["song", "channel_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO safety_violations SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ratings\").toPandas()\ndf[[\"authors\", \"agency_id\"]].to_sql(\"community_development_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "ratings", "columns": null}], "writes": [{"table": "community_development_projects", "columns": ["authors", "agency_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.dwd_payments_full SELECT a.gendername, b.donorid FROM tasks a JOIN mart.shipments_delta b ON a.count_date = b.count_date\"\n", "labels": {"reads": [{"table": "tasks", "columns": null}, {"table": "mart.shipments_delta", "columns": null}], "writes": [{"table": "dwd.dwd_payments_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO tokyo_motor_show SELECT institution_name, competition, local_authority FROM textile_sourcing WHERE institution_name > 350;\nEOF\n", "labels": {"reads": [{"table": "textile_sourcing", "columns": ["institution_name", "competition", "local_authority"]}], "writes": [{"table": "tokyo_motor_show", "columns": ["institution_name", "competition", "local_authority"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ingredient SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT report_type, traveler_id FROM wastewatertreatment LIMIT 245\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO exhibition_visits SELECT therapist_id, heritage_site FROM permian_basin WHERE therapist_id > 194\")\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": ["report_type", "traveler_id"]}, {"table": "permian_basin", "columns": ["therapist_id", "heritage_site"]}], "writes": [{"table": "exhibition_visits", "columns": ["therapist_id", "heritage_site"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg_payments_hourly\"\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table green_energy_lending_programs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "green_energy_lending_programs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hardware_colours, driverid FROM diversity\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"skincare_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "diversity", "columns": ["hardware_colours", "driverid"]}], "writes": [{"table": "skincare_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.moisture_level > 263).all()\n# src table: dw_vendors_di\nengine.execute(\"INSERT INTO safety_research SELECT * FROM dw_vendors_di\")\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": null}], "writes": [{"table": "safety_research", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO diversification_projects SELECT date_of_enrolment, productionrate FROM albums WHERE date_of_enrolment > 309\"\n", "labels": {"reads": [{"table": "albums", "columns": ["date_of_enrolment", "productionrate"]}], "writes": [{"table": "diversification_projects", "columns": ["date_of_enrolment", "productionrate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO pediatricians SELECT a.data_usage, b.min_dew_point_f FROM open_pedagogy_courses a JOIN driver b ON a.community_center_id = b.community_center_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "open_pedagogy_courses", "columns": null}, {"table": "driver", "columns": null}], "writes": [{"table": "pediatricians", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT garment_material, added_date FROM camera_lens LIMIT 33\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "camera_lens", "columns": ["garment_material", "added_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT disaster_id, metric FROM hires\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"marine_life_sightings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hires", "columns": ["disaster_id", "metric"]}], "writes": [{"table": "marine_life_sightings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT opening_year, facility_code FROM check_ins\", engine)\nif not rows:\n    logger.warning('empty result')\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"member\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "check_ins", "columns": ["opening_year", "facility_code"]}], "writes": [{"table": "member", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.astronaut_name > 478).all()\n# src table: mart.mart_users\nengine.execute(\"INSERT INTO province.human_rights_data SELECT * FROM mart.mart_users\")\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": null}], "writes": [{"table": "province.human_rights_data", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"taj_mahal_visitors\")\nsave_to_warehouse(df, \"agricultural_innovations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": null}], "writes": [{"table": "agricultural_innovations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO circular_supply_chain_products SELECT * FROM legacy\ncur.execute(\"SELECT goal_date, awayteamid FROM mart_orders_di LIMIT 248\")\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": ["goal_date", "awayteamid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO militarybases SELECT unitprice, payment_type_code FROM fleets WHERE unitprice > 431\")\n", "labels": {"reads": [{"table": "fleets", "columns": ["unitprice", "payment_type_code"]}], "writes": [{"table": "militarybases", "columns": ["unitprice", "payment_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO menu_items SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vol_id, crane_id FROM transportation_per_country\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"teams_mascots\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": ["vol_id", "crane_id"]}], "writes": [{"table": "teams_mascots", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO clinic_2022 SELECT sustainabilityrating, status, menu_category FROM healthcare_access_v2 WHERE sustainabilityrating > 386\"\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["sustainabilityrating", "status", "menu_category"]}], "writes": [{"table": "clinic_2022", "columns": ["sustainabilityrating", "status", "menu_category"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO songs SELECT tot_cred, labor_id, customer_type_code, acidity FROM projects WHERE tot_cred > 439\"\n", "labels": {"reads": [{"table": "projects", "columns": ["tot_cred", "labor_id", "customer_type_code", "acidity"]}], "writes": [{"table": "songs", "columns": ["tot_cred", "labor_id", "customer_type_code", "acidity"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table producersnewmexico --columns member_name,year_opened --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "producersnewmexico", "columns": ["member_name", "year_opened"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table multimodal_trips --target-dir /tmp/land\n", "labels": {"reads": [{"table": "multimodal_trips", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT head, trip_city FROM regulatory_compliance\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"maintenance_requests\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "regulatory_compliance", "columns": ["head", "trip_city"]}], "writes": [{"table": "maintenance_requests", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"hydro_power\")\nwrite_to_output(df, \"supplychainemployees\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "hydro_power", "columns": null}], "writes": [{"table": "supplychainemployees", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO economic_diversification_projects SELECT field_id, f_id FROM veteran_stats WHERE field_id > 434\"\n", "labels": {"reads": [{"table": "veteran_stats", "columns": ["field_id", "f_id"]}], "writes": [{"table": "economic_diversification_projects", "columns": ["field_id", "f_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"scientists\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"virtual_tourism\")\n", "labels": {"reads": [{"table": "scientists", "columns": null}], "writes": [{"table": "virtual_tourism", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_shipments\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads_sessions_di\")\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": null}], "writes": [{"table": "ads_sessions_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"exhibition\")\nsink_to_warehouse(df, \"urban_farms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "exhibition", "columns": null}], "writes": [{"table": "urban_farms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.users SELECT materialid, birth_country, eliminated_by FROM donors_region WHERE materialid > 60\"\n", "labels": {"reads": [{"table": "donors_region", "columns": ["materialid", "birth_country", "eliminated_by"]}], "writes": [{"table": "stg.users", "columns": ["materialid", "birth_country", "eliminated_by"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"railway\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "railway", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO communityevents SELECT product, creation_year, destination_state FROM city_labor_cost WHERE product > 282\")\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": ["product", "creation_year", "destination_state"]}], "writes": [{"table": "communityevents", "columns": ["product", "creation_year", "destination_state"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO doctors SELECT a.refugee_name, b.hosts FROM categories a JOIN labor_unions b ON a.max_age = b.max_age\"\n", "labels": {"reads": [{"table": "categories", "columns": null}, {"table": "labor_unions", "columns": null}], "writes": [{"table": "doctors", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT phone_number, program_type FROM broadband_providers LIMIT 54\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ods.ods_member_point_delta SELECT booking_date, purchases, university FROM hotel_business_partnerships WHERE booking_date > 58\")\n", "labels": {"reads": [{"table": "broadband_providers", "columns": ["phone_number", "program_type"]}, {"table": "hotel_business_partnerships", "columns": ["booking_date", "purchases", "university"]}], "writes": [{"table": "ods.ods_member_point_delta", "columns": ["booking_date", "purchases", "university"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO community_health_workers SELECT a.wage, b.hometeam FROM billstatus a JOIN product_review b ON a.kids = b.kids\"\n", "labels": {"reads": [{"table": "billstatus", "columns": null}, {"table": "product_review", "columns": null}], "writes": [{"table": "community_health_workers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mappinglengths SELECT starting_year, prod_date, country, intervention_type FROM studentaccommodations WHERE starting_year > 472\"\n", "labels": {"reads": [{"table": "studentaccommodations", "columns": ["starting_year", "prod_date", "country", "intervention_type"]}], "writes": [{"table": "mappinglengths", "columns": ["starting_year", "prod_date", "country", "intervention_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workout\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO supportservices SELECT a.asset_name, b.date_and_date FROM ods.ods_member_point_df a JOIN shops b ON a.market_details = b.market_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.ods_member_point_df", "columns": null}, {"table": "shops", "columns": null}], "writes": [{"table": "supportservices", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ingredient_sourcing SELECT funding_amount, bank_id FROM support_tickets WHERE funding_amount > 214\"\n", "labels": {"reads": [{"table": "support_tickets", "columns": ["funding_amount", "bank_id"]}], "writes": [{"table": "ingredient_sourcing", "columns": ["funding_amount", "bank_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table investor --target-dir /tmp/land\n", "labels": {"reads": [{"table": "investor", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"player_award\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "player_award", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"shariah_financing\").toPandas()\ndf[[\"order_status_code\", \"effort\"]].to_sql(\"ods.ods_payments_full\", engine, index=False)\n", "labels": {"reads": [{"table": "shariah_financing", "columns": null}], "writes": [{"table": "ods.ods_payments_full", "columns": ["order_status_code", "effort"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.dwd_orders_daily SELECT a.carbon_footprint, b.water_depth FROM ads.events a JOIN stg.stg_campaigns b ON a.class_president_vote = b.class_president_vote\"\n", "labels": {"reads": [{"table": "ads.events", "columns": null}, {"table": "stg.stg_campaigns", "columns": null}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"tokyo_water_consumption\")\nwrite_to_target(df, \"artifact_analysis\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tokyo_water_consumption", "columns": null}], "writes": [{"table": "artifact_analysis", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dw.dw_events_di SELECT * FROM legacy\ncur.execute(\"SELECT trip_end_time, transactions FROM textileworkers LIMIT 34\")\n", "labels": {"reads": [{"table": "textileworkers", "columns": ["trip_end_time", "transactions"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table electricvehiclestats --columns avg_depth,instrument --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "electricvehiclestats", "columns": ["avg_depth", "instrument"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO communityhealthworkers SELECT grantamount, founding_year, warehouse_name FROM retailers WHERE grantamount > 413\"\n", "labels": {"reads": [{"table": "retailers", "columns": ["grantamount", "founding_year", "warehouse_name"]}], "writes": [{"table": "communityhealthworkers", "columns": ["grantamount", "founding_year", "warehouse_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bioprocesses SELECT a.founder_identifies_as_lgbtq, b.drugname FROM bookings a JOIN landfills b ON a.ai_id = b.ai_id\"\n", "labels": {"reads": [{"table": "bookings", "columns": null}, {"table": "landfills", "columns": null}], "writes": [{"table": "bioprocesses", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO electric_buses SELECT order_item_id, wastetype, payment_date FROM circuits WHERE order_item_id > 136\")\n", "labels": {"reads": [{"table": "circuits", "columns": ["order_item_id", "wastetype", "payment_date"]}], "writes": [{"table": "electric_buses", "columns": ["order_item_id", "wastetype", "payment_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT visit_details, venue FROM mining_companies LIMIT 41\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO india_solar_power SELECT address_line_1, plant_name, production_quantity FROM gymnast WHERE address_line_1 > 383\")\n", "labels": {"reads": [{"table": "mining_companies", "columns": ["visit_details", "venue"]}, {"table": "gymnast", "columns": ["address_line_1", "plant_name", "production_quantity"]}], "writes": [{"table": "india_solar_power", "columns": ["address_line_1", "plant_name", "production_quantity"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO satellitematerials SELECT a.price, b.route FROM concert a JOIN hospital_equipment b ON a.annual_entry_exit = b.annual_entry_exit\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "concert", "columns": null}, {"table": "hospital_equipment", "columns": null}], "writes": [{"table": "satellitematerials", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"maintenance_schedule\")\nsrc.write.insertInto(\"playergamehistory\", overwrite=True)\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": null}], "writes": [{"table": "playergamehistory", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contract_amount > 393).all()\n# src table: gamereviews\nengine.execute(\"INSERT INTO marine_species_status SELECT * FROM gamereviews\")\n", "labels": {"reads": [{"table": "gamereviews", "columns": null}], "writes": [{"table": "marine_species_status", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.team_name > 172).all()\n# src table: nutritionfacts\nengine.execute(\"INSERT INTO voting_record SELECT * FROM nutritionfacts\")\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": null}], "writes": [{"table": "voting_record", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO carbonoffsetinitiatives SELECT a.labor_cost, b.stars FROM coral_reefs a JOIN candidate_assessments b ON a.esg_factor = b.esg_factor\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "coral_reefs", "columns": null}, {"table": "candidate_assessments", "columns": null}], "writes": [{"table": "carbonoffsetinitiatives", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO canada_cosmetics_preferences (totalamount, event_attendance) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "canada_cosmetics_preferences", "columns": ["totalamount", "event_attendance"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT principal_activities, training_id FROM co2_emissions LIMIT 274\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "co2_emissions", "columns": ["principal_activities", "training_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.mart_device_log_delta SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model india_ingredient_sourcing depends on factories\ndbt run --select india_ingredient_sourcing --vars '{\"source_table\":\"factories\"}'\n", "labels": {"reads": [{"table": "factories", "columns": null}], "writes": [{"table": "india_ingredient_sourcing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO route SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nsql = \"INSERT INTO recalls SELECT a.change_date, b.product_category FROM mart.mart_users_delta a JOIN academic_publications b ON a.deliveryid = b.deliveryid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mart.mart_users_delta", "columns": null}, {"table": "academic_publications", "columns": null}], "writes": [{"table": "recalls", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO infrastructure SELECT a.host_city, b.authorder FROM nasa_mars_program a JOIN dwd.campaigns b ON a.driverid = b.driverid\"\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": null}, {"table": "dwd.campaigns", "columns": null}], "writes": [{"table": "infrastructure", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_projects\").toPandas()\ndf[[\"exploited\", \"department\"]].to_sql(\"winter_olympics\", engine, index=False)\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": [{"table": "winter_olympics", "columns": ["exploited", "department"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT workout_type, institution FROM nz_tourism LIMIT 295\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "nz_tourism", "columns": ["workout_type", "institution"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"music_events\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"emissions\")\n", "labels": {"reads": [{"table": "music_events", "columns": null}], "writes": [{"table": "emissions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"fertilizer\")\nsrc.write.insertInto(\"patient_outcomes\", overwrite=True)\n", "labels": {"reads": [{"table": "fertilizer", "columns": null}], "writes": [{"table": "patient_outcomes", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO contracts (center_id, financially_capable) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "contracts", "columns": ["center_id", "financially_capable"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO awards SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ticketsales\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ods.campaigns_di\")\n", "labels": {"reads": [{"table": "ticketsales", "columns": null}], "writes": [{"table": "ods.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT roomtype, disability_type FROM industry_funding LIMIT 327;\nINSERT INTO dwd.sessions SELECT is_deforested, course_id FROM food_items WHERE is_deforested > 454;\nSQL\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["roomtype", "disability_type"]}, {"table": "food_items", "columns": ["is_deforested", "course_id"]}], "writes": [{"table": "dwd.sessions", "columns": ["is_deforested", "course_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT application, zipcode FROM reservations LIMIT 136\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO mart_campaigns_delta SELECT vaccine_type, hardware_model_name, customer_status_code, contract_id FROM contract_transactions WHERE vaccine_type > 118\")\n", "labels": {"reads": [{"table": "reservations", "columns": ["application", "zipcode"]}, {"table": "contract_transactions", "columns": ["vaccine_type", "hardware_model_name", "customer_status_code", "contract_id"]}], "writes": [{"table": "mart_campaigns_delta", "columns": ["vaccine_type", "hardware_model_name", "customer_status_code", "contract_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table student_lifelong_learning --columns excavationid,product_category --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "student_lifelong_learning", "columns": ["excavationid", "product_category"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO divisions SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"soccer_teams\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mental_health_clinics (student_details, registration_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mental_health_clinics", "columns": ["student_details", "registration_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO comments (booking_date, garment_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "comments", "columns": ["booking_date", "garment_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT healthcareid, site FROM donation\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ads.inventory_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "donation", "columns": ["healthcareid", "site"]}], "writes": [{"table": "ads.inventory_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO atlantic_plate SELECT * FROM legacy\ncur.execute(\"SELECT active_from_date, review_rating FROM textile_waste LIMIT 329\")\n", "labels": {"reads": [{"table": "textile_waste", "columns": ["active_from_date", "review_rating"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO aircraft SELECT founder_veteran, project_type, coalid, athleteid FROM cargos WHERE founder_veteran > 418\"\n", "labels": {"reads": [{"table": "cargos", "columns": ["founder_veteran", "project_type", "coalid", "athleteid"]}], "writes": [{"table": "aircraft", "columns": ["founder_veteran", "project_type", "coalid", "athleteid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM patents\", conn)\ndf.to_sql(\"astronautmedicaldata\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "patents", "columns": null}], "writes": [{"table": "astronautmedicaldata", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_campaigns_daily SELECT * FROM legacy\ncur.execute(\"SELECT sportname, education_id FROM tennis_players LIMIT 411\")\n", "labels": {"reads": [{"table": "tennis_players", "columns": ["sportname", "education_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sale_value > 84).all()\n# src table: transportation_per_country\nengine.execute(\"INSERT INTO animal_population SELECT * FROM transportation_per_country\")\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": null}], "writes": [{"table": "animal_population", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"expenses\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "expenses", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT asset_acquired_date, main_industry FROM smartcityprojects LIMIT 422;\nINSERT INTO animal_population_status SELECT artifactid, case_type, id, supplier_company_id FROM courtcases WHERE artifactid > 244;\nSQL\n", "labels": {"reads": [{"table": "smartcityprojects", "columns": ["asset_acquired_date", "main_industry"]}, {"table": "courtcases", "columns": ["artifactid", "case_type", "id", "supplier_company_id"]}], "writes": [{"table": "animal_population_status", "columns": ["artifactid", "case_type", "id", "supplier_company_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM visitor_exhibition\", conn)\ndf.to_sql(\"train_station\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": null}], "writes": [{"table": "train_station", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM military_contracts\"\n", "labels": {"reads": [{"table": "military_contracts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO military_equipment_maintenance SELECT vendor_id, employeename, artworkname FROM networkdevices WHERE vendor_id > 198\"\n", "labels": {"reads": [{"table": "networkdevices", "columns": ["vendor_id", "employeename", "artworkname"]}], "writes": [{"table": "military_equipment_maintenance", "columns": ["vendor_id", "employeename", "artworkname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM user_ad_interactions\"\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO premises SELECT a.vehicle, b.shipping_agent_name FROM space_exploration a JOIN vr_adopters b ON a.writer = b.writer\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "space_exploration", "columns": null}, {"table": "vr_adopters", "columns": null}], "writes": [{"table": "premises", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_risk_score_full SELECT materialname, mission_count, unit_id FROM carbon_sequestration WHERE materialname > 398\"\n", "labels": {"reads": [{"table": "carbon_sequestration", "columns": ["materialname", "mission_count", "unit_id"]}], "writes": [{"table": "ods.ods_risk_score_full", "columns": ["materialname", "mission_count", "unit_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gradeconversion\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"retail_workers_union\")\n", "labels": {"reads": [{"table": "gradeconversion", "columns": null}], "writes": [{"table": "retail_workers_union", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table reservations --columns year_opened,sport_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "reservations", "columns": ["year_opened", "sport_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mentalhealthparityscores --columns airport_name,decor --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mentalhealthparityscores", "columns": ["airport_name", "decor"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table operations --columns operationname,subject_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "operations", "columns": ["operationname", "subject_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"policy\")\nsrc.write.insertInto(\"trees\", overwrite=True)\n", "labels": {"reads": [{"table": "policy", "columns": null}], "writes": [{"table": "trees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO space_agencies_2 SELECT document_id, course_type, employmentdate FROM healthcareaccess WHERE document_id > 390\"\n", "labels": {"reads": [{"table": "healthcareaccess", "columns": ["document_id", "course_type", "employmentdate"]}], "writes": [{"table": "space_agencies_2", "columns": ["document_id", "course_type", "employmentdate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table routes --columns waste_generation,contract_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "routes", "columns": ["waste_generation", "contract_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.profits_in_billion > 235).all()\n# src table: ods_products_delta\nengine.execute(\"INSERT INTO screen_mode SELECT * FROM ods_products_delta\")\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": null}], "writes": [{"table": "screen_mode", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"peacekeeping_units\")\nwrite_to_warehouse(df, \"workout\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "peacekeeping_units", "columns": null}], "writes": [{"table": "workout", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wastewater_treatment_plants\").toPandas()\ndf[[\"partner_id\", \"eia_date\"]].to_sql(\"mart.mart_campaigns_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "wastewater_treatment_plants", "columns": null}], "writes": [{"table": "mart.mart_campaigns_daily", "columns": ["partner_id", "eia_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO threats SELECT * FROM legacy\ncur.execute(\"SELECT organization_name, claim_amount FROM climate_monitoring_stations LIMIT 419\")\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": ["organization_name", "claim_amount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table daily_oil_production --target-dir /tmp/land\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO council_tax SELECT a.mean_humidity, b.songname FROM militarypersonnel a JOIN urban_initiatives b ON a.platformname = b.platformname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": null}, {"table": "urban_initiatives", "columns": null}], "writes": [{"table": "council_tax", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO program_funding_2 SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sustainability_id, routename FROM medical_facilities_nyc LIMIT 145;\nINSERT INTO ref_attraction_types SELECT color_code, art, coupon_id, share_in_percent FROM communication_scores WHERE color_code > 413;\nSQL\n", "labels": {"reads": [{"table": "medical_facilities_nyc", "columns": ["sustainability_id", "routename"]}, {"table": "communication_scores", "columns": ["color_code", "art", "coupon_id", "share_in_percent"]}], "writes": [{"table": "ref_attraction_types", "columns": ["color_code", "art", "coupon_id", "share_in_percent"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO submersible_dives SELECT stock, funding_source FROM management WHERE stock > 370\")\n", "labels": {"reads": [{"table": "management", "columns": ["stock", "funding_source"]}], "writes": [{"table": "submersible_dives", "columns": ["stock", "funding_source"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthequitymetrics\").toPandas()\ndf[[\"home_team_three_point\", \"event\"]].to_sql(\"restaurants\", engine, index=False)\n", "labels": {"reads": [{"table": "healthequitymetrics", "columns": null}], "writes": [{"table": "restaurants", "columns": ["home_team_three_point", "event"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model travel_advisory depends on on_call\ndbt build --select travel_advisory --vars '{\"source_table\":\"on_call\"}'\n", "labels": {"reads": [{"table": "on_call", "columns": null}], "writes": [{"table": "travel_advisory", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ethics_violations (contract_date, case_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ethics_violations", "columns": ["contract_date", "case_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO marine_species_status SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO virtual_tour_revenue SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO causes_insert_2 SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO interventions SELECT * FROM legacy\ncur.execute(\"SELECT indigenous, update_date FROM india_solar_power LIMIT 328\")\n", "labels": {"reads": [{"table": "india_solar_power", "columns": ["indigenous", "update_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO rural_resources SELECT book_id, rooms, species_name FROM mart.mart_users_delta WHERE book_id > 481\"\n", "labels": {"reads": [{"table": "mart.mart_users_delta", "columns": ["book_id", "rooms", "species_name"]}], "writes": [{"table": "rural_resources", "columns": ["book_id", "rooms", "species_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model scan_dates depends on public_works_projects\ndbt build -s scan_dates --vars '{\"source_table\":\"public_works_projects\"}'\n", "labels": {"reads": [{"table": "public_works_projects", "columns": null}], "writes": [{"table": "scan_dates", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"orders\")\nsrc.write.insertInto(\"mining_companies\", overwrite=True)\n", "labels": {"reads": [{"table": "orders", "columns": null}], "writes": [{"table": "mining_companies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"countryintelligenceops\")\ndump_to_store(df, \"whale_sightings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "countryintelligenceops", "columns": null}], "writes": [{"table": "whale_sightings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO food_justice SELECT rehab_date, isfirstattendee, painting_name, strategy_id FROM chemicalproducts WHERE rehab_date > 94\"\n", "labels": {"reads": [{"table": "chemicalproducts", "columns": ["rehab_date", "isfirstattendee", "painting_name", "strategy_id"]}], "writes": [{"table": "food_justice", "columns": ["rehab_date", "isfirstattendee", "painting_name", "strategy_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT account_number, market_value_billion FROM sports\", engine)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"economic_diversification_efforts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sports", "columns": ["account_number", "market_value_billion"]}], "writes": [{"table": "economic_diversification_efforts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT composer, ram_mib FROM routes LIMIT 99;\nINSERT INTO defense_contractors SELECT shipmenttype, date_from, experience, stars FROM food_items WHERE shipmenttype > 257;\nSQL\n", "labels": {"reads": [{"table": "routes", "columns": ["composer", "ram_mib"]}, {"table": "food_items", "columns": ["shipmenttype", "date_from", "experience", "stars"]}], "writes": [{"table": "defense_contractors", "columns": ["shipmenttype", "date_from", "experience", "stars"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods_vendors_daily SELECT technology, contract_value FROM gender WHERE technology > 281\"\n", "labels": {"reads": [{"table": "gender", "columns": ["technology", "contract_value"]}], "writes": [{"table": "ods_vendors_daily", "columns": ["technology", "contract_value"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model experts depends on marine_life_research_stations\ndbt run --select experts --vars 'source: marine_life_research_stations'\n", "labels": {"reads": [{"table": "marine_life_research_stations", "columns": null}], "writes": [{"table": "experts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"web_client_accelerator\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"healthcare_centers\")\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": null}], "writes": [{"table": "healthcare_centers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT startup_name, manufacturer_name FROM artwork LIMIT 116\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "artwork", "columns": ["startup_name", "manufacturer_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO financial_capability_program SELECT company_id, calendar, part_fault_id, incidentid FROM virtual_tour_offers WHERE company_id > 183\"\n", "labels": {"reads": [{"table": "virtual_tour_offers", "columns": ["company_id", "calendar", "part_fault_id", "incidentid"]}], "writes": [{"table": "financial_capability_program", "columns": ["company_id", "calendar", "part_fault_id", "incidentid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sustainability_metrics SELECT permit_number, account_balance, goal_id, date_in_locaton_to FROM research WHERE permit_number > 375\"\n", "labels": {"reads": [{"table": "research", "columns": ["permit_number", "account_balance", "goal_id", "date_in_locaton_to"]}], "writes": [{"table": "sustainability_metrics", "columns": ["permit_number", "account_balance", "goal_id", "date_in_locaton_to"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO spending SELECT a.market_value, b.opponent_id FROM mappinglengths a JOIN dp_articles b ON a.category = b.category\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mappinglengths", "columns": null}, {"table": "dp_articles", "columns": null}], "writes": [{"table": "spending", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO workforce_development SELECT custid, roomid, warehouse_name, milestone FROM wastewater_plants WHERE custid > 146\"], check=True)\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": ["custid", "roomid", "warehouse_name", "milestone"]}], "writes": [{"table": "workforce_development", "columns": ["custid", "roomid", "warehouse_name", "milestone"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table authorship --target-dir /tmp/land\n", "labels": {"reads": [{"table": "authorship", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO transportation_per_country (sales_billion, investor_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "transportation_per_country", "columns": ["sales_billion", "investor_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT rig_name, color_code FROM safetytestingcounts LIMIT 385\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO autonomousvehicleaccidents SELECT fname, artworkname FROM vulnerabilities WHERE fname > 218\")\n", "labels": {"reads": [{"table": "safetytestingcounts", "columns": ["rig_name", "color_code"]}, {"table": "vulnerabilities", "columns": ["fname", "artworkname"]}], "writes": [{"table": "autonomousvehicleaccidents", "columns": ["fname", "artworkname"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM haircare_sales\", conn)\ndf.to_sql(\"cultivators\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "haircare_sales", "columns": null}], "writes": [{"table": "cultivators", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO comments SELECT a.workoutdate, b.catalog_level_number FROM policyanalysis a JOIN climate_finance_organizations b ON a.itemid = b.itemid\"\n", "labels": {"reads": [{"table": "policyanalysis", "columns": null}, {"table": "climate_finance_organizations", "columns": null}], "writes": [{"table": "comments", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO public.forest_stats SELECT a.document_description, b.hashtags FROM party_forms a JOIN dw.dw_events_di b ON a.quarter = b.quarter\"\n", "labels": {"reads": [{"table": "party_forms", "columns": null}, {"table": "dw.dw_events_di", "columns": null}], "writes": [{"table": "public.forest_stats", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO restorative_justice_center (review_id, characteristic_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "restorative_justice_center", "columns": ["review_id", "characteristic_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO store SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT worker_id, decor FROM underwater_trenches LIMIT 336\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "underwater_trenches", "columns": ["worker_id", "decor"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO e_scooter_trips SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT condition_id, jobcategory FROM diversification_projects LIMIT 487\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "diversification_projects", "columns": ["condition_id", "jobcategory"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO underwater_cables SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.dws_member_point_di (inspectionscore, phone_number) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_member_point_di", "columns": ["inspectionscore", "phone_number"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO user_reactions SELECT a.watertemp, b.case_burden FROM college a JOIN virtual_tours_oceania b ON a.building_type = b.building_type\"\n", "labels": {"reads": [{"table": "college", "columns": null}, {"table": "virtual_tours_oceania", "columns": null}], "writes": [{"table": "user_reactions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO expenses (origin_city, color) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "expenses", "columns": ["origin_city", "color"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO communityengagements SELECT a.tot_cred, b.model_id FROM open_pedagogy_enrollment a JOIN carbon_offset_south_america b ON a.schedule_date = b.schedule_date\"\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}, {"table": "carbon_offset_south_america", "columns": null}], "writes": [{"table": "communityengagements", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM hydro_plants\", conn)\ndf.to_sql(\"files\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "hydro_plants", "columns": null}], "writes": [{"table": "files", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO crops SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO acceptance SELECT wind_speed_mph, physical, furniture_id, launch_agency FROM agricultural_projects WHERE wind_speed_mph > 426\"\n", "labels": {"reads": [{"table": "agricultural_projects", "columns": ["wind_speed_mph", "physical", "furniture_id", "launch_agency"]}], "writes": [{"table": "acceptance", "columns": ["wind_speed_mph", "physical", "furniture_id", "launch_agency"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"atlantic_marine_life\").toPandas()\ndf[[\"num_of_audience\", \"police_force\"]].to_sql(\"election\", engine, index=False)\n", "labels": {"reads": [{"table": "atlantic_marine_life", "columns": null}], "writes": [{"table": "election", "columns": ["num_of_audience", "police_force"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_exploration\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.stg_clicks_delta\")\n", "labels": {"reads": [{"table": "space_exploration", "columns": null}], "writes": [{"table": "stg.stg_clicks_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO bi.bi_sessions_hourly SELECT registered_date, platformname, truck_licence_number FROM mart.mart_risk_score_hourly WHERE registered_date > 381\"\n", "labels": {"reads": [{"table": "mart.mart_risk_score_hourly", "columns": ["registered_date", "platformname", "truck_licence_number"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["registered_date", "platformname", "truck_licence_number"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT field, grantid FROM bay_area_properties LIMIT 290\")\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO dw.dw_inventory_delta SELECT num_employees, secretary_vote, max_salary, headquarters FROM continent WHERE num_employees > 192\")\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": ["field", "grantid"]}, {"table": "continent", "columns": ["num_employees", "secretary_vote", "max_salary", "headquarters"]}], "writes": [{"table": "dw.dw_inventory_delta", "columns": ["num_employees", "secretary_vote", "max_salary", "headquarters"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"ocean_salinity\")\nexport_to_sink(df, \"artists\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": null}], "writes": [{"table": "artists", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO producers SELECT a.art, b.acidity FROM climate_communication a JOIN total_capacity b ON a.initiative_region = b.initiative_region\"\n", "labels": {"reads": [{"table": "climate_communication", "columns": null}, {"table": "total_capacity", "columns": null}], "writes": [{"table": "producers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"social_impact_bonds\")\nsrc.write.insertInto(\"teachers\", overwrite=True)\n", "labels": {"reads": [{"table": "social_impact_bonds", "columns": null}], "writes": [{"table": "teachers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"taxi_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "taxi_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"threats\").toPandas()\ndf[[\"lieutenant_governor\", \"dock_id\"]].to_sql(\"infantmortalitydata\", engine, index=False)\n", "labels": {"reads": [{"table": "threats", "columns": null}], "writes": [{"table": "infantmortalitydata", "columns": ["lieutenant_governor", "dock_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO singer SELECT ngo_id, production_usage FROM bank_info WHERE ngo_id > 210\"\n", "labels": {"reads": [{"table": "bank_info", "columns": ["ngo_id", "production_usage"]}], "writes": [{"table": "singer", "columns": ["ngo_id", "production_usage"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dispensaries\").toPandas()\ndf[[\"transaction_date\", \"call_time\"]].to_sql(\"city_properties\", engine, index=False)\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}], "writes": [{"table": "city_properties", "columns": ["transaction_date", "call_time"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table visitor_statistics --columns assessmentid,dockingid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "visitor_statistics", "columns": ["assessmentid", "dockingid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_energy_lending_programs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"productsafety\")\n", "labels": {"reads": [{"table": "green_energy_lending_programs", "columns": null}], "writes": [{"table": "productsafety", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table artists --target-dir /tmp/land\n", "labels": {"reads": [{"table": "artists", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_member_point_di\")\nsrc.write.insertInto(\"initiatives_3\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_member_point_di", "columns": null}], "writes": [{"table": "initiatives_3", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO professional_development SELECT department_id, guest_id, attendanceid, season FROM forests WHERE department_id > 319\"\n", "labels": {"reads": [{"table": "forests", "columns": ["department_id", "guest_id", "attendanceid", "season"]}], "writes": [{"table": "professional_development", "columns": ["department_id", "guest_id", "attendanceid", "season"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.area > 74).all()\n# src table: restaurant\nengine.execute(\"INSERT INTO lots SELECT * FROM restaurant\")\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": [{"table": "lots", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"permit\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table stg_orders_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical_composition\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "chemical_composition", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM waterconservationinitiatives\"\n", "labels": {"reads": [{"table": "waterconservationinitiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO seafood SELECT trial_name, tree_id FROM invoice WHERE trial_name > 220\"\n", "labels": {"reads": [{"table": "invoice", "columns": ["trial_name", "tree_id"]}], "writes": [{"table": "seafood", "columns": ["trial_name", "tree_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT investment_amount, period FROM elimination LIMIT 241;\nINSERT INTO mentalhealthprofessional SELECT patient_name, event_type, cinema_id FROM pollution_control_initiatives WHERE patient_name > 462;\nSQL\n", "labels": {"reads": [{"table": "elimination", "columns": ["investment_amount", "period"]}, {"table": "pollution_control_initiatives", "columns": ["patient_name", "event_type", "cinema_id"]}], "writes": [{"table": "mentalhealthprofessional", "columns": ["patient_name", "event_type", "cinema_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table project_timeline --columns claim_status_name,gdp --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "project_timeline", "columns": ["claim_status_name", "gdp"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO checking SELECT patentexpirationdate, platformname, pettype FROM furniture WHERE patentexpirationdate > 118\"\n", "labels": {"reads": [{"table": "furniture", "columns": ["patentexpirationdate", "platformname", "pettype"]}], "writes": [{"table": "checking", "columns": ["patentexpirationdate", "platformname", "pettype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO restaurant_revenue SELECT supplychainid, allergy, review_score FROM trade_history WHERE supplychainid > 230;\nEOF\n", "labels": {"reads": [{"table": "trade_history", "columns": ["supplychainid", "allergy", "review_score"]}], "writes": [{"table": "restaurant_revenue", "columns": ["supplychainid", "allergy", "review_score"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO manufacturingplants SELECT offense, cropname, job_title_code, address_id FROM expenses WHERE offense > 94\"\n", "labels": {"reads": [{"table": "expenses", "columns": ["offense", "cropname", "job_title_code", "address_id"]}], "writes": [{"table": "manufacturingplants", "columns": ["offense", "cropname", "job_title_code", "address_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO city.community_policing SELECT programname, numpieces, tx_id FROM soilmoisturedata WHERE programname > 1\"\n", "labels": {"reads": [{"table": "soilmoisturedata", "columns": ["programname", "numpieces", "tx_id"]}], "writes": [{"table": "city.community_policing", "columns": ["programname", "numpieces", "tx_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO salesdata SELECT workoutid, membername, event FROM inclusive_housing WHERE workoutid > 8\"], check=True)\n", "labels": {"reads": [{"table": "inclusive_housing", "columns": ["workoutid", "membername", "event"]}], "writes": [{"table": "salesdata", "columns": ["workoutid", "membername", "event"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.creator > 261).all()\n# src table: ma_inspections\nengine.execute(\"INSERT INTO stg.stg_users_full SELECT * FROM ma_inspections\")\n", "labels": {"reads": [{"table": "ma_inspections", "columns": null}], "writes": [{"table": "stg.stg_users_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM demographics\", conn)\ndf.to_sql(\"culturalpractices\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "demographics", "columns": null}], "writes": [{"table": "culturalpractices", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model waste_generation_city_v2 depends on climate_communication_projects\ndbt run --select waste_generation_city_v2 --vars '{\"src\":\"climate_communication_projects\"}'\n", "labels": {"reads": [{"table": "climate_communication_projects", "columns": null}], "writes": [{"table": "waste_generation_city_v2", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO fish_biomass SELECT * FROM legacy\ncur.execute(\"SELECT hours_contributed, recycling_rate FROM military_expenditure LIMIT 35\")\n", "labels": {"reads": [{"table": "military_expenditure", "columns": ["hours_contributed", "recycling_rate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO workforce_development SELECT programoutcomeid, assessmentid FROM military_innovation WHERE programoutcomeid > 330\"\n", "labels": {"reads": [{"table": "military_innovation", "columns": ["programoutcomeid", "assessmentid"]}], "writes": [{"table": "workforce_development", "columns": ["programoutcomeid", "assessmentid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_source(ctx, \"veterans\")\nexport_to_store(df, \"patienttreatments\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "veterans", "columns": null}], "writes": [{"table": "patienttreatments", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model catalogs depends on publications\ndbt run -s catalogs --vars '{\"src\":\"publications\"}'\n", "labels": {"reads": [{"table": "publications", "columns": null}], "writes": [{"table": "catalogs", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO biomes SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_refunds_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"birds\")\n", "labels": {"reads": [{"table": "dws.dws_refunds_hourly", "columns": null}], "writes": [{"table": "birds", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sessionid, contributorid FROM rental LIMIT 207\")\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO asset_parts SELECT time_of_day, lipstick_id, waste_id FROM office_locations WHERE time_of_day > 400\")\n", "labels": {"reads": [{"table": "rental", "columns": ["sessionid", "contributorid"]}, {"table": "office_locations", "columns": ["time_of_day", "lipstick_id", "waste_id"]}], "writes": [{"table": "asset_parts", "columns": ["time_of_day", "lipstick_id", "waste_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dw_vendors_di SELECT incidentdate, performancedate FROM musical WHERE incidentdate > 280\"\n", "labels": {"reads": [{"table": "musical", "columns": ["incidentdate", "performancedate"]}], "writes": [{"table": "dw_vendors_di", "columns": ["incidentdate", "performancedate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ota_revenue SELECT a.eid, b.crs_description FROM ods.ods_campaigns_delta a JOIN building b ON a.floor_exercise_points = b.floor_exercise_points\"\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": null}, {"table": "building", "columns": null}], "writes": [{"table": "ota_revenue", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table policy_feedback --columns order_item_status,provider_parity_score --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "policy_feedback", "columns": ["order_item_status", "provider_parity_score"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO recycling_centers SELECT budget_type_description, attack_date, arrival_time, agreementid FROM renewable_projects WHERE budget_type_description > 499\"\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["budget_type_description", "attack_date", "arrival_time", "agreementid"]}], "writes": [{"table": "recycling_centers", "columns": ["budget_type_description", "attack_date", "arrival_time", "agreementid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.ads_shipments_delta\", conn)\ndf.to_sql(\"basketball_teams\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_shipments_delta", "columns": null}], "writes": [{"table": "basketball_teams", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO fabricinventory SELECT a.founder_group, b.production_rate FROM visual_arts a JOIN rural_clinics b ON a.measurement = b.measurement\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "visual_arts", "columns": null}, {"table": "rural_clinics", "columns": null}], "writes": [{"table": "fabricinventory", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"microfinance_clients\").toPandas()\ndf[[\"complaint_id\", \"attendee_race\"]].to_sql(\"culturalpractices\", engine, index=False)\n", "labels": {"reads": [{"table": "microfinance_clients", "columns": null}], "writes": [{"table": "culturalpractices", "columns": ["complaint_id", "attendee_race"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ocean_temperatures --columns launched_year,restorative_justice --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ocean_temperatures", "columns": ["launched_year", "restorative_justice"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO drama_workshop_groups SELECT registered_date, organic, duration FROM prescribes WHERE registered_date > 462\")\n", "labels": {"reads": [{"table": "prescribes", "columns": ["registered_date", "organic", "duration"]}], "writes": [{"table": "drama_workshop_groups", "columns": ["registered_date", "organic", "duration"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO malicious_activity (campaign_id, safety_rating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "malicious_activity", "columns": ["campaign_id", "safety_rating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO farmers (ngo_id, value) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "farmers", "columns": ["ngo_id", "value"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT employee_name, restock_date FROM animal_population\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"eventdates\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "animal_population", "columns": ["employee_name", "restock_date"]}], "writes": [{"table": "eventdates", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model subscribers depends on mart.mart_events_di\ndbt run --models subscribers --vars 'source: mart.mart_events_di'\n", "labels": {"reads": [{"table": "mart.mart_events_di", "columns": null}], "writes": [{"table": "subscribers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"equipment_maintenance\").toPandas()\ndf[[\"case_status\", \"starttime\"]].to_sql(\"classroom\", engine, index=False)\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": null}], "writes": [{"table": "classroom", "columns": ["case_status", "starttime"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO spacecraft_manufacturing (country_name, maintenance_contract_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "spacecraft_manufacturing", "columns": ["country_name", "maintenance_contract_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO calibration_data2 SELECT a.grant_date, b.image_data FROM product a JOIN traditionalarts b ON a.thing_id = b.thing_id\"\n", "labels": {"reads": [{"table": "product", "columns": null}, {"table": "traditionalarts", "columns": null}], "writes": [{"table": "calibration_data2", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainability_fact SELECT * FROM legacy\ncur.execute(\"SELECT attribute_data_type, acidity_level FROM album LIMIT 112\")\n", "labels": {"reads": [{"table": "album", "columns": ["attribute_data_type", "acidity_level"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"socialimpactinvestments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"smart_contracts_table\")\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shariah_financing (communityid, communityname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shariah_financing", "columns": ["communityid", "communityname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO gamesales SELECT a.permitdate, b.dataset FROM aircraftsquadrons a JOIN team_revenue b ON a.founder_ethnicity = b.founder_ethnicity\"\n", "labels": {"reads": [{"table": "aircraftsquadrons", "columns": null}, {"table": "team_revenue", "columns": null}], "writes": [{"table": "gamesales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO route_fares SELECT a.detention_type_code, b.hotel_chain_id FROM mining_operations a JOIN travel_advisory b ON a.culture = b.culture\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mining_operations", "columns": null}, {"table": "travel_advisory", "columns": null}], "writes": [{"table": "route_fares", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT channel_code, emp_hiredate FROM eu_data_usage\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"marine_life_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["channel_code", "emp_hiredate"]}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gametype, donation_date FROM policyholders LIMIT 72\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "policyholders", "columns": ["gametype", "donation_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"community_programs\")\nsrc.write.insertInto(\"ai_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "community_programs", "columns": null}], "writes": [{"table": "ai_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donor\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donor", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.assigned_to_staff_id > 486).all()\n# src table: carbon_offsets.carbon_offsets\nengine.execute(\"INSERT INTO port_visits SELECT * FROM carbon_offsets.carbon_offsets\")\n", "labels": {"reads": [{"table": "carbon_offsets.carbon_offsets", "columns": null}], "writes": [{"table": "port_visits", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ocean_pollution\")\nsrc.write.insertInto(\"pets\", overwrite=True)\n", "labels": {"reads": [{"table": "ocean_pollution", "columns": null}], "writes": [{"table": "pets", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT grant_type, worker_id FROM policy_advocacy LIMIT 225\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "policy_advocacy", "columns": ["grant_type", "worker_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT event_id, billing_country FROM weather LIMIT 218\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO irrigation_systems SELECT sqft, gender FROM voyages WHERE sqft > 110\")\n", "labels": {"reads": [{"table": "weather", "columns": ["event_id", "billing_country"]}, {"table": "voyages", "columns": ["sqft", "gender"]}], "writes": [{"table": "irrigation_systems", "columns": ["sqft", "gender"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mexico_regions SELECT a.mouse_id, b.item_type FROM appointments a JOIN infantmortalitydata b ON a.order_shipping_charges = b.order_shipping_charges\"\n", "labels": {"reads": [{"table": "appointments", "columns": null}, {"table": "infantmortalitydata", "columns": null}], "writes": [{"table": "mexico_regions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart_campaigns_delta SELECT * FROM legacy\ncur.execute(\"SELECT average_age, successful_cb FROM staff_department_assignments LIMIT 271\")\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["average_age", "successful_cb"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spaceexploration SELECT a.rehab_date, b.region_code FROM mart.mart_device_log_hourly a JOIN defense_spending_3 b ON a.artworkyear = b.artworkyear\"\n", "labels": {"reads": [{"table": "mart.mart_device_log_hourly", "columns": null}, {"table": "defense_spending_3", "columns": null}], "writes": [{"table": "spaceexploration", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_costs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.bi_vendors_di\")\n", "labels": {"reads": [{"table": "labor_costs", "columns": null}], "writes": [{"table": "bi.bi_vendors_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT policy, safetytestdate FROM ods.ods_sessions_df\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"sales_region\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": ["policy", "safetytestdate"]}], "writes": [{"table": "sales_region", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"monthly_temp\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"hospital\")\n", "labels": {"reads": [{"table": "monthly_temp", "columns": null}], "writes": [{"table": "hospital", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM studentaccommodations\", conn)\ndf.to_sql(\"circulation_history\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "studentaccommodations", "columns": null}], "writes": [{"table": "circulation_history", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO thefttypes SELECT a.plantid, b.host_city FROM assets a JOIN tour_guides b ON a.num_libraries = b.num_libraries\"\n", "labels": {"reads": [{"table": "assets", "columns": null}, {"table": "tour_guides", "columns": null}], "writes": [{"table": "thefttypes", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO adrprograms SELECT wildlife_type_id, causename, brand, relationship FROM employeepromotions WHERE wildlife_type_id > 137\"\n", "labels": {"reads": [{"table": "employeepromotions", "columns": ["wildlife_type_id", "causename", "brand", "relationship"]}], "writes": [{"table": "adrprograms", "columns": ["wildlife_type_id", "causename", "brand", "relationship"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"ads_exposure_hourly\")\nsave_to_sink(df, \"membership_register_branch\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": null}], "writes": [{"table": "membership_register_branch", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model satellitedata depends on rural_feeder_roads\ndbt build --models satellitedata --vars 'source: rural_feeder_roads'\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": null}], "writes": [{"table": "satellitedata", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT employer_organisation_id, paintingid FROM volunteerhours\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"reviews\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "volunteerhours", "columns": ["employer_organisation_id", "paintingid"]}], "writes": [{"table": "reviews", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT treatment, line_id FROM environmental_impact\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"vehicle_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "environmental_impact", "columns": ["treatment", "line_id"]}], "writes": [{"table": "vehicle_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table smartcitycosts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO legal_technology_funding (eliminated_by, silver) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "legal_technology_funding", "columns": ["eliminated_by", "silver"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT center, aircraft FROM virtual_tour_engagement LIMIT 219\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO invoices SELECT donor_state, signupdate, impact_id FROM regulatory_frameworks WHERE donor_state > 284\")\n", "labels": {"reads": [{"table": "virtual_tour_engagement", "columns": ["center", "aircraft"]}, {"table": "regulatory_frameworks", "columns": ["donor_state", "signupdate", "impact_id"]}], "writes": [{"table": "invoices", "columns": ["donor_state", "signupdate", "impact_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO assignedto SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"creative_ai_applications\").toPandas()\ndf[[\"shipmentid\", \"assessment_score\"]].to_sql(\"rural_economy_2\", engine, index=False)\n", "labels": {"reads": [{"table": "creative_ai_applications", "columns": null}], "writes": [{"table": "rural_economy_2", "columns": ["shipmentid", "assessment_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT medicine_id, fda_approved FROM food_safety_inspections LIMIT 314\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": ["medicine_id", "fda_approved"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT played, sitename FROM channel LIMIT 426\")\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO hall_of_fame SELECT completion_date, reviewscore, start_therapy, monthlyactiveusers FROM safetyincidents WHERE completion_date > 422\")\n", "labels": {"reads": [{"table": "channel", "columns": ["played", "sitename"]}, {"table": "safetyincidents", "columns": ["completion_date", "reviewscore", "start_therapy", "monthlyactiveusers"]}], "writes": [{"table": "hall_of_fame", "columns": ["completion_date", "reviewscore", "start_therapy", "monthlyactiveusers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO tb_reports SELECT hospital_name, grant_amount, spacecraft_model, materialid FROM diversion_programs WHERE hospital_name > 351\"\n", "labels": {"reads": [{"table": "diversion_programs", "columns": ["hospital_name", "grant_amount", "spacecraft_model", "materialid"]}], "writes": [{"table": "tb_reports", "columns": ["hospital_name", "grant_amount", "spacecraft_model", "materialid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sale_country > 311).all()\n# src table: inclusivehousingpolicies\nengine.execute(\"INSERT INTO public.ev_sales SELECT * FROM inclusivehousingpolicies\")\n", "labels": {"reads": [{"table": "inclusivehousingpolicies", "columns": null}], "writes": [{"table": "public.ev_sales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO field SELECT a.num_students, b.agegroup FROM operation a JOIN africa_schema.african_mines b ON a.artistid = b.artistid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "operation", "columns": null}, {"table": "africa_schema.african_mines", "columns": null}], "writes": [{"table": "field", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation\").toPandas()\ndf[[\"number_of_observations\", \"violation_id\"]].to_sql(\"communitydevelopment\", engine, index=False)\n", "labels": {"reads": [{"table": "transportation", "columns": null}], "writes": [{"table": "communitydevelopment", "columns": ["number_of_observations", "violation_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model environmentalimpact depends on ods.ods_users_di\ndbt build -s environmentalimpact --vars 'source: ods.ods_users_di'\n", "labels": {"reads": [{"table": "ods.ods_users_di", "columns": null}], "writes": [{"table": "environmentalimpact", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT team, total_points FROM miningwaterusage LIMIT 150;\nINSERT INTO vulnerabilities SELECT match_id, disaster_id FROM trainingprograms WHERE match_id > 421;\nSQL\n", "labels": {"reads": [{"table": "miningwaterusage", "columns": ["team", "total_points"]}, {"table": "trainingprograms", "columns": ["match_id", "disaster_id"]}], "writes": [{"table": "vulnerabilities", "columns": ["match_id", "disaster_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO ocean_acidity SELECT a.seating, b.famous_title FROM bi.products_daily a JOIN furniture b ON a.num_investments = b.num_investments\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": null}, {"table": "furniture", "columns": null}], "writes": [{"table": "ocean_acidity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO music_festival (course_id, employee) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "music_festival", "columns": ["course_id", "employee"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO smartcitytech SELECT * FROM legacy\ncur.execute(\"SELECT resource_name, fish_count FROM field LIMIT 152\")\n", "labels": {"reads": [{"table": "field", "columns": ["resource_name", "fish_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.mart_refunds_hourly SELECT allergy, expedition_name FROM electric_vehicles WHERE allergy > 255\"], check=True)\n", "labels": {"reads": [{"table": "electric_vehicles", "columns": ["allergy", "expedition_name"]}], "writes": [{"table": "mart.mart_refunds_hourly", "columns": ["allergy", "expedition_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO excavations (area_id, prod_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "excavations", "columns": ["area_id", "prod_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"haircaresales\")\nsrc.write.insertInto(\"student_access\", overwrite=True)\n", "labels": {"reads": [{"table": "haircaresales", "columns": null}], "writes": [{"table": "student_access", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO participants SELECT * FROM legacy\ncur.execute(\"SELECT trainingtitle, count_time FROM diplomacy_events LIMIT 323\")\n", "labels": {"reads": [{"table": "diplomacy_events", "columns": ["trainingtitle", "count_time"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_exposure_hourly SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"security_incidents\")\nsrc.write.insertInto(\"third_party_companies\", overwrite=True)\n", "labels": {"reads": [{"table": "security_incidents", "columns": null}], "writes": [{"table": "third_party_companies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ancient_artifacts\"\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO restaurant_type SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO social_good_projects SELECT international_passengers, operationdate, sustainability_initiative_id, main_services FROM community_education_programs WHERE international_passengers > 48;\nEOF\n", "labels": {"reads": [{"table": "community_education_programs", "columns": ["international_passengers", "operationdate", "sustainability_initiative_id", "main_services"]}], "writes": [{"table": "social_good_projects", "columns": ["international_passengers", "operationdate", "sustainability_initiative_id", "main_services"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gameplatforms SELECT age_group_id, schedule, port_code FROM threatintelligence WHERE age_group_id > 405\"\n", "labels": {"reads": [{"table": "threatintelligence", "columns": ["age_group_id", "schedule", "port_code"]}], "writes": [{"table": "gameplatforms", "columns": ["age_group_id", "schedule", "port_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO public_transport.passenger_count SELECT policy_type_code, recycler_id, state_code FROM representative WHERE policy_type_code > 24\"\n", "labels": {"reads": [{"table": "representative", "columns": ["policy_type_code", "recycler_id", "state_code"]}], "writes": [{"table": "public_transport.passenger_count", "columns": ["policy_type_code", "recycler_id", "state_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organicproducts\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "organicproducts", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT trip_end_time, asset_id FROM fare_segments LIMIT 9;\nINSERT INTO european_healthcare SELECT journal, assessment_date, preference_score, resource_id FROM influencers WHERE journal > 9;\nSQL\n", "labels": {"reads": [{"table": "fare_segments", "columns": ["trip_end_time", "asset_id"]}, {"table": "influencers", "columns": ["journal", "assessment_date", "preference_score", "resource_id"]}], "writes": [{"table": "european_healthcare", "columns": ["journal", "assessment_date", "preference_score", "resource_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model school_enrollment depends on bike_share\ndbt run --select school_enrollment --vars '{\"source_table\":\"bike_share\"}'\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": [{"table": "school_enrollment", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO festival_detail (time_hour, faculty) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "festival_detail", "columns": ["time_hour", "faculty"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO intelligence_agents SELECT host_city, day_of_week, formats, sessionid FROM school_roster WHERE host_city > 475;\nEOF\n", "labels": {"reads": [{"table": "school_roster", "columns": ["host_city", "day_of_week", "formats", "sessionid"]}], "writes": [{"table": "intelligence_agents", "columns": ["host_city", "day_of_week", "formats", "sessionid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model atlantic_ocean_fish depends on ai_safety_incidents\ndbt run --models atlantic_ocean_fish --vars '{\"src\":\"ai_safety_incidents\"}'\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": null}], "writes": [{"table": "atlantic_ocean_fish", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table recalls --columns workshop_name,restaurantid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "recalls", "columns": ["workshop_name", "restaurantid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO gold (incidenttype, productionrate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "gold", "columns": ["incidenttype", "productionrate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.ods_payments_full SELECT thefttype, date_of_publication, dissolved_oxygen FROM calibration_data2 WHERE thefttype > 276;\nEOF\n", "labels": {"reads": [{"table": "calibration_data2", "columns": ["thefttype", "date_of_publication", "dissolved_oxygen"]}], "writes": [{"table": "ods.ods_payments_full", "columns": ["thefttype", "date_of_publication", "dissolved_oxygen"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM wind_energy_projects\"\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO high_risk SELECT text, garment_name, assessment_score, u_id FROM sales_region WHERE text > 246\"\n", "labels": {"reads": [{"table": "sales_region", "columns": ["text", "garment_name", "assessment_score", "u_id"]}], "writes": [{"table": "high_risk", "columns": ["text", "garment_name", "assessment_score", "u_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT inspection_time, archaeologistid FROM building\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"station\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "building", "columns": ["inspection_time", "archaeologistid"]}], "writes": [{"table": "station", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cases (sector_id, potency) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cases", "columns": ["sector_id", "potency"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_orders SELECT appointment_date, scientist, date_problem_reported FROM mining_operation_data WHERE appointment_date > 267\"\n", "labels": {"reads": [{"table": "mining_operation_data", "columns": ["appointment_date", "scientist", "date_problem_reported"]}], "writes": [{"table": "ads.ads_orders", "columns": ["appointment_date", "scientist", "date_problem_reported"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO defense_spending_3 SELECT attribute_name, amount_claimed, detection_id FROM debris WHERE attribute_name > 247\"], check=True)\n", "labels": {"reads": [{"table": "debris", "columns": ["attribute_name", "amount_claimed", "detection_id"]}], "writes": [{"table": "defense_spending_3", "columns": ["attribute_name", "amount_claimed", "detection_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hotel_id, theatrename FROM water_treatment_facilities\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"nomination\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "water_treatment_facilities", "columns": ["hotel_id", "theatrename"]}], "writes": [{"table": "nomination", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO genetic.projects SELECT a.brand_mentioned, b.company_name FROM stg.risk_score_di a JOIN staff_roles b ON a.is_safe = b.is_safe\"\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}, {"table": "staff_roles", "columns": null}], "writes": [{"table": "genetic.projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO team_members SELECT health_equity_metric_2, scoreid, mental_health_rating FROM instructor WHERE health_equity_metric_2 > 197\"\n", "labels": {"reads": [{"table": "instructor", "columns": ["health_equity_metric_2", "scoreid", "mental_health_rating"]}], "writes": [{"table": "team_members", "columns": ["health_equity_metric_2", "scoreid", "mental_health_rating"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_payments_df\").toPandas()\ndf[[\"class_section\", \"attribute_id\"]].to_sql(\"vehiclemodels\", engine, index=False)\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": null}], "writes": [{"table": "vehiclemodels", "columns": ["class_section", "attribute_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM department_stores\", conn)\ndf.to_sql(\"sustainability_initiatives\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "department_stores", "columns": null}], "writes": [{"table": "sustainability_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"ota_revenue\")\ndump_to_target(df, \"dwd.dwd_risk_score_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ota_revenue", "columns": null}], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"networkdevices\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"electric_buses\")\n", "labels": {"reads": [{"table": "networkdevices", "columns": null}], "writes": [{"table": "electric_buses", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO researchers SELECT a.vendor_state, b.supplierid FROM suburbs a JOIN unesco_intangible_heritage b ON a.asset_name = b.asset_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "suburbs", "columns": null}, {"table": "unesco_intangible_heritage", "columns": null}], "writes": [{"table": "researchers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM temperatureanomalies\"\n", "labels": {"reads": [{"table": "temperatureanomalies", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"justice_schemas.legal_tech_providers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"enrolled_in\")\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": null}], "writes": [{"table": "enrolled_in", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nsql = \"INSERT INTO ods.coupon_use SELECT a.explainability_score, b.program_type FROM ads.refunds_delta a JOIN gamegenres b ON a.field = b.field\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": null}, {"table": "gamegenres", "columns": null}], "writes": [{"table": "ods.coupon_use", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO red_line SELECT reviewscore, therapy_date FROM academic_publications WHERE reviewscore > 433;\nEOF\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["reviewscore", "therapy_date"]}], "writes": [{"table": "red_line", "columns": ["reviewscore", "therapy_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO clothingsales SELECT status, strainid, playtime FROM diseases WHERE status > 364\"\n", "labels": {"reads": [{"table": "diseases", "columns": ["status", "strainid", "playtime"]}], "writes": [{"table": "clothingsales", "columns": ["status", "strainid", "playtime"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.user_id > 33).all()\n# src table: labels\nengine.execute(\"INSERT INTO research SELECT * FROM labels\")\n", "labels": {"reads": [{"table": "labels", "columns": null}], "writes": [{"table": "research", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM container_ships\"\n", "labels": {"reads": [{"table": "container_ships", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cargoid, centerid FROM prison LIMIT 416\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO lenders SELECT enr, node_id, eventid, art_type FROM ngo_funding WHERE enr > 22\")\n", "labels": {"reads": [{"table": "prison", "columns": ["cargoid", "centerid"]}, {"table": "ngo_funding", "columns": ["enr", "node_id", "eventid", "art_type"]}], "writes": [{"table": "lenders", "columns": ["enr", "node_id", "eventid", "art_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"trainers\")\nsink_to_sink(df, \"climate_mitigation_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "trainers", "columns": null}], "writes": [{"table": "climate_mitigation_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO industry_funding (incident_id, billing) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "industry_funding", "columns": ["incident_id", "billing"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT conservation_status, num_developments FROM ads_cart_item_hourly\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"canada_cosmetics_preferences\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads_cart_item_hourly", "columns": ["conservation_status", "num_developments"]}], "writes": [{"table": "canada_cosmetics_preferences", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cargo_tracking --columns team_name,complaint_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cargo_tracking", "columns": ["team_name", "complaint_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd_sessions_df SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"nursing_homes\")\nupsert_to_warehouse(df, \"light_rail_lines\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "nursing_homes", "columns": null}], "writes": [{"table": "light_rail_lines", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dw_vendors_di depends on instructors\ndbt run -s dw_vendors_di --vars '{\"src\":\"instructors\"}'\n", "labels": {"reads": [{"table": "instructors", "columns": null}], "writes": [{"table": "dw_vendors_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT status, impact_id FROM cargo_data LIMIT 159\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO freightforwarding SELECT reo_type, attraction_type_code FROM spacecraft_temperatures WHERE reo_type > 352\")\n", "labels": {"reads": [{"table": "cargo_data", "columns": ["status", "impact_id"]}, {"table": "spacecraft_temperatures", "columns": ["reo_type", "attraction_type_code"]}], "writes": [{"table": "freightforwarding", "columns": ["reo_type", "attraction_type_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO region SELECT budgetid, spacecraft_id, institution, refugee_id FROM hospitallocations WHERE budgetid > 345\"], check=True)\n", "labels": {"reads": [{"table": "hospitallocations", "columns": ["budgetid", "spacecraft_id", "institution", "refugee_id"]}], "writes": [{"table": "region", "columns": ["budgetid", "spacecraft_id", "institution", "refugee_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ods.ods_coupon_use_di SELECT campaign_id, review_text FROM foodaid WHERE campaign_id > 166\"\n", "labels": {"reads": [{"table": "foodaid", "columns": ["campaign_id", "review_text"]}], "writes": [{"table": "ods.ods_coupon_use_di", "columns": ["campaign_id", "review_text"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rebounds\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_refunds_hourly\")\n", "labels": {"reads": [{"table": "rebounds", "columns": null}], "writes": [{"table": "dws.dws_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bus_fare_collection\", conn)\ndf.to_sql(\"vendorfabrics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": null}], "writes": [{"table": "vendorfabrics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tree_habitat_associations SELECT practices, journal FROM phishing_attempts WHERE practices > 136\"\n", "labels": {"reads": [{"table": "phishing_attempts", "columns": ["practices", "journal"]}], "writes": [{"table": "tree_habitat_associations", "columns": ["practices", "journal"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO online_platform SELECT restaurantname, release_date, ai_adoption_date, detected_at FROM traveler WHERE restaurantname > 289\"], check=True)\n", "labels": {"reads": [{"table": "traveler", "columns": ["restaurantname", "release_date", "ai_adoption_date", "detected_at"]}], "writes": [{"table": "online_platform", "columns": ["restaurantname", "release_date", "ai_adoption_date", "detected_at"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO biosensors.patents SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT platform, conservation_status FROM iron_ore_production LIMIT 160\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["platform", "conservation_status"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ethicalaibudget --columns functional_area_code,playlist_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ethicalaibudget", "columns": ["functional_area_code", "playlist_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO al_jazeera_data SELECT bicycle_id, inspection_date, membername, donationid FROM company WHERE bicycle_id > 162\"\n", "labels": {"reads": [{"table": "company", "columns": ["bicycle_id", "inspection_date", "membername", "donationid"]}], "writes": [{"table": "al_jazeera_data", "columns": ["bicycle_id", "inspection_date", "membername", "donationid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"baseball_teams\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"strains\")\n", "labels": {"reads": [{"table": "baseball_teams", "columns": null}], "writes": [{"table": "strains", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO genre_songs SELECT * FROM legacy\ncur.execute(\"SELECT circuitid, cargo_weight FROM food_justice_orgs LIMIT 140\")\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": ["circuitid", "cargo_weight"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO renewableenergy SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO landfillcapacitybycountry SELECT workoutid, trip_date FROM wind_farms WHERE workoutid > 349;\nEOF\n", "labels": {"reads": [{"table": "wind_farms", "columns": ["workoutid", "trip_date"]}], "writes": [{"table": "landfillcapacitybycountry", "columns": ["workoutid", "trip_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO biodiversity SELECT * FROM legacy\ncur.execute(\"SELECT province_id, review_id FROM bi.bi_events_daily LIMIT 23\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["province_id", "review_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model device_log_df depends on candidate_assessments\ndbt build --models device_log_df --vars '{\"source_table\":\"candidate_assessments\"}'\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}], "writes": [{"table": "device_log_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO disabilitysupportprograms SELECT customer, programname, garment_name, detention_type_code FROM healthcare_access_v2 WHERE customer > 309\"\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["customer", "programname", "garment_name", "detention_type_code"]}], "writes": [{"table": "disabilitysupportprograms", "columns": ["customer", "programname", "garment_name", "detention_type_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"recycling_rates_oceania\")\npush_to_output(df, \"authors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "recycling_rates_oceania", "columns": null}], "writes": [{"table": "authors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO eco_materials SELECT a.winery, b.farmname FROM arctic_research a JOIN ocean_floor_depth b ON a.show_times_per_day = b.show_times_per_day\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}, {"table": "ocean_floor_depth", "columns": null}], "writes": [{"table": "eco_materials", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO maintenancerequests SELECT a.regionid, b.rural_area FROM invoice_lines a JOIN interventions b ON a.leadershiptraining = b.leadershiptraining\"\n", "labels": {"reads": [{"table": "invoice_lines", "columns": null}, {"table": "interventions", "columns": null}], "writes": [{"table": "maintenancerequests", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO operate_company SELECT log_entry_description, membership_id, date_of_ceremony FROM carbon_offsets WHERE log_entry_description > 216\"\n", "labels": {"reads": [{"table": "carbon_offsets", "columns": ["log_entry_description", "membership_id", "date_of_ceremony"]}], "writes": [{"table": "operate_company", "columns": ["log_entry_description", "membership_id", "date_of_ceremony"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"num_employees\").toPandas()\ndf[[\"transact_date\", \"breed\"]].to_sql(\"bi.bi_sessions_df\", engine, index=False)\n", "labels": {"reads": [{"table": "num_employees", "columns": null}], "writes": [{"table": "bi.bi_sessions_df", "columns": ["transact_date", "breed"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT number_city_affected, trench_name FROM disaster_response_donations\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"ads.orders_daily\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": ["number_city_affected", "trench_name"]}], "writes": [{"table": "ads.orders_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO user_genre SELECT request_id, problem_id, book_club_id FROM stg.risk_score_hourly WHERE request_id > 284\"\n", "labels": {"reads": [{"table": "stg.risk_score_hourly", "columns": ["request_id", "problem_id", "book_club_id"]}], "writes": [{"table": "user_genre", "columns": ["request_id", "problem_id", "book_club_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT committee, date_of_latest_logon FROM city_labor_cost\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"causes_insert_2\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": ["committee", "date_of_latest_logon"]}], "writes": [{"table": "causes_insert_2", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO climate_investments SELECT attack_id, is_dessert, dlocation, cb_year FROM field5 WHERE attack_id > 19;\nEOF\n", "labels": {"reads": [{"table": "field5", "columns": ["attack_id", "is_dessert", "dlocation", "cb_year"]}], "writes": [{"table": "climate_investments", "columns": ["attack_id", "is_dessert", "dlocation", "cb_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.mart_users_delta --columns chargeable_amount,method_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_users_delta", "columns": ["chargeable_amount", "method_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO infantmortalitydata SELECT a.lesson_status_code, b.artifactname FROM safety_data a JOIN waterconservation b ON a.university_type = b.university_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "safety_data", "columns": null}, {"table": "waterconservation", "columns": null}], "writes": [{"table": "infantmortalitydata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO program_history SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model smartcities depends on facility\ndbt build -s smartcities --vars '{\"source_table\":\"facility\"}'\n", "labels": {"reads": [{"table": "facility", "columns": null}], "writes": [{"table": "smartcities", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM program_budget\"\n", "labels": {"reads": [{"table": "program_budget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT restypedescription, checkin FROM eventattendance LIMIT 2;\nINSERT INTO trucks SELECT time, subject_area_id, undergraduate, retailer FROM all_documents WHERE time > 66;\nSQL\n", "labels": {"reads": [{"table": "eventattendance", "columns": ["restypedescription", "checkin"]}, {"table": "all_documents", "columns": ["time", "subject_area_id", "undergraduate", "retailer"]}], "writes": [{"table": "trucks", "columns": ["time", "subject_area_id", "undergraduate", "retailer"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO production_data SELECT showid, builder, funding_amount, menu_item FROM police_emergencies WHERE showid > 95\"\n", "labels": {"reads": [{"table": "police_emergencies", "columns": ["showid", "builder", "funding_amount", "menu_item"]}], "writes": [{"table": "production_data", "columns": ["showid", "builder", "funding_amount", "menu_item"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO drought_data SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_vendors SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO defense_diplomacy SELECT * FROM legacy\ncur.execute(\"SELECT average_attendance, cultural_diversity FROM chargingstations LIMIT 399\")\n", "labels": {"reads": [{"table": "chargingstations", "columns": ["average_attendance", "cultural_diversity"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO researchgrants SELECT loadingend, emp_id, program_id FROM music_database WHERE loadingend > 69\"\n", "labels": {"reads": [{"table": "music_database", "columns": ["loadingend", "emp_id", "program_id"]}], "writes": [{"table": "researchgrants", "columns": ["loadingend", "emp_id", "program_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sales_id, aircraft FROM mart.mart_refunds_hourly LIMIT 279;\nINSERT INTO team_franchise SELECT target_u_id, electoral_register_id, posts_per_day, reported FROM government.region WHERE target_u_id > 339;\nSQL\n", "labels": {"reads": [{"table": "mart.mart_refunds_hourly", "columns": ["sales_id", "aircraft"]}, {"table": "government.region", "columns": ["target_u_id", "electoral_register_id", "posts_per_day", "reported"]}], "writes": [{"table": "team_franchise", "columns": ["target_u_id", "electoral_register_id", "posts_per_day", "reported"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tech_workers_union SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_users_di SELECT menuitemid, issues FROM factories_africa WHERE menuitemid > 11\"], check=True)\n", "labels": {"reads": [{"table": "factories_africa", "columns": ["menuitemid", "issues"]}], "writes": [{"table": "stg.stg_users_di", "columns": ["menuitemid", "issues"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"prescribes\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "prescribes", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"fund_investments\")\nsink_to_output(df, \"meals\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fund_investments", "columns": null}], "writes": [{"table": "meals", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sales_quarterly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cars\")\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": null}], "writes": [{"table": "cars", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO strandings SELECT a.min_age, b.conferenceid FROM store a JOIN recreation_centers b ON a.agegroup = b.agegroup\"\n", "labels": {"reads": [{"table": "store", "columns": null}, {"table": "recreation_centers", "columns": null}], "writes": [{"table": "strandings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO intelligencesatellites SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO channel SELECT * FROM legacy\ncur.execute(\"SELECT patientid, vehicle_model FROM threat_intel LIMIT 322\")\n", "labels": {"reads": [{"table": "threat_intel", "columns": ["patientid", "vehicle_model"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"philadelphia_police_emergencies\").toPandas()\ndf[[\"log_entry_date\", \"player_api_id\"]].to_sql(\"sfc_articles\", engine, index=False)\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": [{"table": "sfc_articles", "columns": ["log_entry_date", "player_api_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO athlete_stats SELECT rig_id, pixels, organic_matter FROM stg.refunds_hourly WHERE rig_id > 274\"\n", "labels": {"reads": [{"table": "stg.refunds_hourly", "columns": ["rig_id", "pixels", "organic_matter"]}], "writes": [{"table": "athlete_stats", "columns": ["rig_id", "pixels", "organic_matter"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO musical SELECT good_or_bad_customer, market_id FROM brazil_projects WHERE good_or_bad_customer > 16\"], check=True)\n", "labels": {"reads": [{"table": "brazil_projects", "columns": ["good_or_bad_customer", "market_id"]}], "writes": [{"table": "musical", "columns": ["good_or_bad_customer", "market_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO chip_model SELECT hospital_id, facility_name, fate FROM ads.ads_campaigns_full WHERE hospital_id > 401;\nEOF\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": ["hospital_id", "facility_name", "fate"]}], "writes": [{"table": "chip_model", "columns": ["hospital_id", "facility_name", "fate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ca_menu_items SELECT budget, rating_id FROM dws_coupon_use WHERE budget > 155\"\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["budget", "rating_id"]}], "writes": [{"table": "ca_menu_items", "columns": ["budget", "rating_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mart_refunds SELECT artwork, implementation_date, avg_depth, emergency_type FROM commercialbuildings WHERE artwork > 162;\nEOF\n", "labels": {"reads": [{"table": "commercialbuildings", "columns": ["artwork", "implementation_date", "avg_depth", "emergency_type"]}], "writes": [{"table": "mart_refunds", "columns": ["artwork", "implementation_date", "avg_depth", "emergency_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_users_hourly SELECT attendance, updatedate FROM behavior_incident WHERE attendance > 439\"\n", "labels": {"reads": [{"table": "behavior_incident", "columns": ["attendance", "updatedate"]}], "writes": [{"table": "ads.ads_users_hourly", "columns": ["attendance", "updatedate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO expenditure SELECT categoryid, stream_date FROM bi.events_delta WHERE categoryid > 371\"\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": ["categoryid", "stream_date"]}], "writes": [{"table": "expenditure", "columns": ["categoryid", "stream_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO dwd.dwd_campaigns_df SELECT a.maintenance_contract_id, b.timestamp FROM cosmetic_sales a JOIN london.stations b ON a.vehicle_flight_number = b.vehicle_flight_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "cosmetic_sales", "columns": null}, {"table": "london.stations", "columns": null}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stores_2 SELECT * FROM legacy\ncur.execute(\"SELECT subscribe_date, truck_details FROM scientists LIMIT 441\")\n", "labels": {"reads": [{"table": "scientists", "columns": ["subscribe_date", "truck_details"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO harvest_permits SELECT date_assigned_from, date_problem_reported, contract_end, functional_area_description FROM classroom WHERE date_assigned_from > 249;\nEOF\n", "labels": {"reads": [{"table": "classroom", "columns": ["date_assigned_from", "date_problem_reported", "contract_end", "functional_area_description"]}], "writes": [{"table": "harvest_permits", "columns": ["date_assigned_from", "date_problem_reported", "contract_end", "functional_area_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO performance_scores SELECT a.country_of_origin, b.saleid FROM machines a JOIN causes b ON a.apt_id = b.apt_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "machines", "columns": null}, {"table": "causes", "columns": null}], "writes": [{"table": "performance_scores", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_events_hourly SELECT a.campus, b.practice_id FROM airportdata a JOIN mart.mart_vendors b ON a.good_or_bad_customer = b.good_or_bad_customer\"\n", "labels": {"reads": [{"table": "airportdata", "columns": null}, {"table": "mart.mart_vendors", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table voyages --columns component_name,platform --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "voyages", "columns": ["component_name", "platform"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO workouts SELECT museum, chip_model, num_developments FROM daily_revenue WHERE museum > 286\"\n", "labels": {"reads": [{"table": "daily_revenue", "columns": ["museum", "chip_model", "num_developments"]}], "writes": [{"table": "workouts", "columns": ["museum", "chip_model", "num_developments"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table ref_service_types --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ref_service_types", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO basketball_teams SELECT phone_id, shariah_compliant_investment_amount, international_passengers, productionrate FROM user_workouts_march WHERE phone_id > 485\"\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": ["phone_id", "shariah_compliant_investment_amount", "international_passengers", "productionrate"]}], "writes": [{"table": "basketball_teams", "columns": ["phone_id", "shariah_compliant_investment_amount", "international_passengers", "productionrate"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO skincareinventory SELECT a.s_id, b.fundingdate FROM emergencies a JOIN supplier_addresses b ON a.date_of_latest_logon = b.date_of_latest_logon\"\n", "labels": {"reads": [{"table": "emergencies", "columns": null}, {"table": "supplier_addresses", "columns": null}], "writes": [{"table": "skincareinventory", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.inventory_df\"\n", "labels": {"reads": [{"table": "stg.inventory_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO coralreefs SELECT * FROM legacy\ncur.execute(\"SELECT discount, race FROM auto_shows LIMIT 417\")\n", "labels": {"reads": [{"table": "auto_shows", "columns": ["discount", "race"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT safetytestdate, musical_id FROM recyclednylongarments LIMIT 50\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "recyclednylongarments", "columns": ["safetytestdate", "musical_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.dws_orders (section_id, call_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_orders", "columns": ["section_id", "call_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO city_department SELECT expertise, cityname, revenueid, co_owner_count FROM winter_olympics WHERE expertise > 363\"\n", "labels": {"reads": [{"table": "winter_olympics", "columns": ["expertise", "cityname", "revenueid", "co_owner_count"]}], "writes": [{"table": "city_department", "columns": ["expertise", "cityname", "revenueid", "co_owner_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO sponsor_trials SELECT a.contributor, b.trainingid FROM dw.dw_orders_hourly a JOIN mineral_extraction_us b ON a.ethical_manufacturing = b.ethical_manufacturing\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dw.dw_orders_hourly", "columns": null}, {"table": "mineral_extraction_us", "columns": null}], "writes": [{"table": "sponsor_trials", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT phone_id, outcome_date FROM stg.campaigns_daily LIMIT 324;\nINSERT INTO commercialbuildings SELECT continent, card_number, well_type, classtype FROM satellites WHERE continent > 434;\nSQL\n", "labels": {"reads": [{"table": "stg.campaigns_daily", "columns": ["phone_id", "outcome_date"]}, {"table": "satellites", "columns": ["continent", "card_number", "well_type", "classtype"]}], "writes": [{"table": "commercialbuildings", "columns": ["continent", "card_number", "well_type", "classtype"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO genetics.projects SELECT affirmative, expedition_name, medium FROM lead_mines WHERE affirmative > 72\"\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["affirmative", "expedition_name", "medium"]}], "writes": [{"table": "genetics.projects", "columns": ["affirmative", "expedition_name", "medium"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table otas --columns elevation,genrename --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "otas", "columns": ["elevation", "genrename"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO container_receipts SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table crop_temperature --columns plan_id,hire_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "crop_temperature", "columns": ["plan_id", "hire_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO agriculturalinnovations SELECT labor_id, date_incident_start, exhibitions FROM dws.payments_delta WHERE labor_id > 479\"\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": ["labor_id", "date_incident_start", "exhibitions"]}], "writes": [{"table": "agriculturalinnovations", "columns": ["labor_id", "date_incident_start", "exhibitions"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_coupon_use_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"satelliteimagery\")\n", "labels": {"reads": [{"table": "dwd_coupon_use_hourly", "columns": null}], "writes": [{"table": "satelliteimagery", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO train_station SELECT license_type, class, metric FROM overwatch_scores WHERE license_type > 368\"\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": ["license_type", "class", "metric"]}], "writes": [{"table": "train_station", "columns": ["license_type", "class", "metric"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"forestry_practices\")\nsrc.write.insertInto(\"acidification_data\", overwrite=True)\n", "labels": {"reads": [{"table": "forestry_practices", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO suburbs (ai_customer_service, last_year) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "suburbs", "columns": ["ai_customer_service", "last_year"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO intelligenceoperations SELECT church_id, trainingtype, half, lieutenant_governor FROM dwd.dwd_payments_di WHERE church_id > 57;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_payments_di", "columns": ["church_id", "trainingtype", "half", "lieutenant_governor"]}], "writes": [{"table": "intelligenceoperations", "columns": ["church_id", "trainingtype", "half", "lieutenant_governor"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO org_comms SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hourid, emissions FROM exhibition_visits LIMIT 355\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO waste_management_projects SELECT discovery_date, asset_disposed_date, arrival_time, report_date FROM driverstandings WHERE discovery_date > 13\")\n", "labels": {"reads": [{"table": "exhibition_visits", "columns": ["hourid", "emissions"]}, {"table": "driverstandings", "columns": ["discovery_date", "asset_disposed_date", "arrival_time", "report_date"]}], "writes": [{"table": "waste_management_projects", "columns": ["discovery_date", "asset_disposed_date", "arrival_time", "report_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nsql = \"INSERT INTO militarydrones SELECT a.fairtrade, b.stu_hrs FROM recycling_rates_oceania a JOIN mart.mart_products_hourly b ON a.issue_month = b.issue_month\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recycling_rates_oceania", "columns": null}, {"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "militarydrones", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO coral_reefs SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM crime_incidents\"\n", "labels": {"reads": [{"table": "crime_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO participation SELECT prof_office, workoutdate, part_fault_id, genre FROM units WHERE prof_office > 467\"\n", "labels": {"reads": [{"table": "units", "columns": ["prof_office", "workoutdate", "part_fault_id", "genre"]}], "writes": [{"table": "participation", "columns": ["prof_office", "workoutdate", "part_fault_id", "genre"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mineral_extraction_us\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mineral_extraction_us", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO user_ad_interactions SELECT num_hotels, clinic_type, max_salary, spent FROM ads_users_hourly WHERE num_hotels > 385\")\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": ["num_hotels", "clinic_type", "max_salary", "spent"]}], "writes": [{"table": "user_ad_interactions", "columns": ["num_hotels", "clinic_type", "max_salary", "spent"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO beverages SELECT subscription_start_date, account_balance FROM usdaviolations WHERE subscription_start_date > 247\"\n", "labels": {"reads": [{"table": "usdaviolations", "columns": ["subscription_start_date", "account_balance"]}], "writes": [{"table": "beverages", "columns": ["subscription_start_date", "account_balance"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd.campaigns SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table diversification_projects --columns ocean,ship_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "diversification_projects", "columns": ["ocean", "ship_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table shipment_data --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shipment_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hoursperweek, app_name FROM drills\", engine)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"police_officers_tx\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "drills", "columns": ["hoursperweek", "app_name"]}], "writes": [{"table": "police_officers_tx", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO postseason SELECT * FROM legacy\ncur.execute(\"SELECT funder, singer_id FROM vehicle LIMIT 76\")\n", "labels": {"reads": [{"table": "vehicle", "columns": ["funder", "singer_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"causes\")\nsrc.write.insertInto(\"category_revenue\", overwrite=True)\n", "labels": {"reads": [{"table": "causes", "columns": null}], "writes": [{"table": "category_revenue", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_pd_hours\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_shipments_full\")\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": null}], "writes": [{"table": "dws.dws_shipments_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM economic_diversification_efforts\"\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table tree_habitat_associations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.dwd_device_log_delta\", conn)\ndf.to_sql(\"precipitation_data\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_device_log_delta", "columns": null}], "writes": [{"table": "precipitation_data", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO parks SELECT part_id, lname, source_u_id FROM traffic WHERE part_id > 294\"\n", "labels": {"reads": [{"table": "traffic", "columns": ["part_id", "lname", "source_u_id"]}], "writes": [{"table": "parks", "columns": ["part_id", "lname", "source_u_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO strategies SELECT a.founded, b.acidity_level FROM dwd.dwd_campaigns a JOIN smartcityprojects b ON a.union_member_id = b.union_member_id\"\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": null}, {"table": "smartcityprojects", "columns": null}], "writes": [{"table": "strategies", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table underwater_cables --columns strategy,tourists --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "underwater_cables", "columns": ["strategy", "tourists"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO eu_data_usage SELECT division, researcher_name FROM assignedto WHERE division > 30\"\n", "labels": {"reads": [{"table": "assignedto", "columns": ["division", "researcher_name"]}], "writes": [{"table": "eu_data_usage", "columns": ["division", "researcher_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO smartcitycosts SELECT adoption_date, lender_id, cargoid FROM energy_prices WHERE adoption_date > 343\"\n", "labels": {"reads": [{"table": "energy_prices", "columns": ["adoption_date", "lender_id", "cargoid"]}], "writes": [{"table": "smartcitycosts", "columns": ["adoption_date", "lender_id", "cargoid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ai_for_social_good (num_pallets, health_equity_metric_1) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ai_for_social_good", "columns": ["num_pallets", "health_equity_metric_1"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT mental_health_score, grant_start_date FROM class LIMIT 64\")\nrows = cur.fetchall()\nimport logging\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "class", "columns": ["mental_health_score", "grant_start_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"agricultural_innovations\")\nsrc.write.insertInto(\"art_exhibit_attendance\", overwrite=True)\n", "labels": {"reads": [{"table": "agricultural_innovations", "columns": null}], "writes": [{"table": "art_exhibit_attendance", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM jobs\"\n", "labels": {"reads": [{"table": "jobs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO chemical_production_5 SELECT semester, employee_name FROM conservation_projects WHERE semester > 95;\nEOF\n", "labels": {"reads": [{"table": "conservation_projects", "columns": ["semester", "employee_name"]}], "writes": [{"table": "chemical_production_5", "columns": ["semester", "employee_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table episodes --columns base_id,genderid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "episodes", "columns": ["base_id", "genderid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dw_member_point_di depends on stg.sessions_full\ndbt build --models dw_member_point_di --vars 'source: stg.sessions_full'\n", "labels": {"reads": [{"table": "stg.sessions_full", "columns": null}], "writes": [{"table": "dw_member_point_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO communitydevelopment SELECT participant_name, product_subcategory, ngo_name FROM fish_biomass WHERE participant_name > 359\")\n", "labels": {"reads": [{"table": "fish_biomass", "columns": ["participant_name", "product_subcategory", "ngo_name"]}], "writes": [{"table": "communitydevelopment", "columns": ["participant_name", "product_subcategory", "ngo_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM sports\"\n", "labels": {"reads": [{"table": "sports", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO train SELECT first_donation_date, metric FROM ads_orders WHERE first_donation_date > 201\"\n", "labels": {"reads": [{"table": "ads_orders", "columns": ["first_donation_date", "metric"]}], "writes": [{"table": "train", "columns": ["first_donation_date", "metric"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_research\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"policyadvocacyevents\")\n", "labels": {"reads": [{"table": "marine_life_research", "columns": null}], "writes": [{"table": "policyadvocacyevents", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO inclusivehousing.affordablehousing SELECT a.election_cycle, b.art_type FROM transportation_union a JOIN dws.dws_clicks_full b ON a.enable_third_party_ads = b.enable_third_party_ads\"\n", "labels": {"reads": [{"table": "transportation_union", "columns": null}, {"table": "dws.dws_clicks_full", "columns": null}], "writes": [{"table": "inclusivehousing.affordablehousing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads.ads_exposure_daily SELECT vehicle_flight_number, alid FROM ods.ods_events_daily WHERE vehicle_flight_number > 293;\nEOF\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": ["vehicle_flight_number", "alid"]}], "writes": [{"table": "ads.ads_exposure_daily", "columns": ["vehicle_flight_number", "alid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table equipment --columns grant_start_date,num_schools --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "equipment", "columns": ["grant_start_date", "num_schools"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT medicine_id, attendeename FROM artist_concerts LIMIT 324\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "artist_concerts", "columns": ["medicine_id", "attendeename"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_payments_df --columns policy_description,statename --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_payments_df", "columns": ["policy_description", "statename"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT coupon_amount, mascot FROM activities LIMIT 89\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "activities", "columns": ["coupon_amount", "mascot"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"ref_calendar\")\nexport_to_output(df, \"stg.stg_products_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ref_calendar", "columns": null}], "writes": [{"table": "stg.stg_products_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO city_department SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO book_club SELECT interaction_type, call_id FROM location WHERE interaction_type > 356\"\n", "labels": {"reads": [{"table": "location", "columns": ["interaction_type", "call_id"]}], "writes": [{"table": "book_club", "columns": ["interaction_type", "call_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO intelligence_agents SELECT * FROM legacy\ncur.execute(\"SELECT members, healthcareid FROM ethics_violations LIMIT 279\")\n", "labels": {"reads": [{"table": "ethics_violations", "columns": ["members", "healthcareid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO manufacturer SELECT patentexpirationdate, tree_type_id FROM equipment_sales WHERE patentexpirationdate > 376\"], check=True)\n", "labels": {"reads": [{"table": "equipment_sales", "columns": ["patentexpirationdate", "tree_type_id"]}], "writes": [{"table": "manufacturer", "columns": ["patentexpirationdate", "tree_type_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO textile_waste SELECT has_spf, appointment_duration FROM ocean WHERE has_spf > 244\"], check=True)\n", "labels": {"reads": [{"table": "ocean", "columns": ["has_spf", "appointment_duration"]}], "writes": [{"table": "textile_waste", "columns": ["has_spf", "appointment_duration"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO docking SELECT dst_apid, prod_date FROM mineral_extraction WHERE dst_apid > 21\"\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": ["dst_apid", "prod_date"]}], "writes": [{"table": "docking", "columns": ["dst_apid", "prod_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fans_merchandise_basketball\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"community_leaders\")\n", "labels": {"reads": [{"table": "fans_merchandise_basketball", "columns": null}], "writes": [{"table": "community_leaders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT openning_year, building_name FROM projecttimelinebybudget\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"mediators\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": ["openning_year", "building_name"]}], "writes": [{"table": "mediators", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_risk_score_df SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investors\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"extraction_methods\")\n", "labels": {"reads": [{"table": "investors", "columns": null}], "writes": [{"table": "extraction_methods", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.incorporated_in > 5).all()\n# src table: sustainable_menu_items\nengine.execute(\"INSERT INTO cyber_incidents SELECT * FROM sustainable_menu_items\")\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": null}], "writes": [{"table": "cyber_incidents", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"bikerental\")\nsink_to_sink(df, \"conservation_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bikerental", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO marine_life_research SELECT a.building_name, b.trip_start_time FROM harvest_permits a JOIN apac_hotel_views b ON a.tonnage = b.tonnage\"\n", "labels": {"reads": [{"table": "harvest_permits", "columns": null}, {"table": "apac_hotel_views", "columns": null}], "writes": [{"table": "marine_life_research", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"manufacturingplants\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"manager_award\")\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": null}], "writes": [{"table": "manager_award", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM classicgame\"\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT noise_level, animal_id FROM stores LIMIT 439\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO city_labor_cost SELECT days_held, party FROM transport WHERE days_held > 459\")\n", "labels": {"reads": [{"table": "stores", "columns": ["noise_level", "animal_id"]}, {"table": "transport", "columns": ["days_held", "party"]}], "writes": [{"table": "city_labor_cost", "columns": ["days_held", "party"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM gamereviews\"\n", "labels": {"reads": [{"table": "gamereviews", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cinema SELECT open_year, population FROM dwd_events_delta WHERE open_year > 500\"\n", "labels": {"reads": [{"table": "dwd_events_delta", "columns": ["open_year", "population"]}], "writes": [{"table": "cinema", "columns": ["open_year", "population"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO factory_water SELECT supplychainid, export_country, customer_last_name, cropid FROM trips WHERE supplychainid > 405\")\n", "labels": {"reads": [{"table": "trips", "columns": ["supplychainid", "export_country", "customer_last_name", "cropid"]}], "writes": [{"table": "factory_water", "columns": ["supplychainid", "export_country", "customer_last_name", "cropid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"cultural_heritage\")\nsrc.write.insertInto(\"otas\", overwrite=True)\n", "labels": {"reads": [{"table": "cultural_heritage", "columns": null}], "writes": [{"table": "otas", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"videos\").toPandas()\ndf[[\"ship_id\", \"chemical_type\"]].to_sql(\"epl_teams\", engine, index=False)\n", "labels": {"reads": [{"table": "videos", "columns": null}], "writes": [{"table": "epl_teams", "columns": ["ship_id", "chemical_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO violations (attendees, group_equity_shareholding) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "violations", "columns": ["attendees", "group_equity_shareholding"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO weights SELECT funding_amount, claim_header_id FROM donations WHERE funding_amount > 420\"\n", "labels": {"reads": [{"table": "donations", "columns": ["funding_amount", "claim_header_id"]}], "writes": [{"table": "weights", "columns": ["funding_amount", "claim_header_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO chemical_composition SELECT a.host_city, b.mgr_start_date FROM waste_data a JOIN marine_species b ON a.custid = b.custid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "waste_data", "columns": null}, {"table": "marine_species", "columns": null}], "writes": [{"table": "chemical_composition", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO product_catalog (dockingid, item_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "product_catalog", "columns": ["dockingid", "item_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO intel_ops SELECT * FROM legacy\ncur.execute(\"SELECT customer_first_name, bill_id FROM view_unit_status LIMIT 145\")\n", "labels": {"reads": [{"table": "view_unit_status", "columns": ["customer_first_name", "bill_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.high_estimate > 181).all()\n# src table: energy_efficiency_programs\nengine.execute(\"INSERT INTO grant SELECT * FROM energy_efficiency_programs\")\n", "labels": {"reads": [{"table": "energy_efficiency_programs", "columns": null}], "writes": [{"table": "grant", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO farm SELECT num_stops, gender_mf FROM news_reporting WHERE num_stops > 83;\nEOF\n", "labels": {"reads": [{"table": "news_reporting", "columns": ["num_stops", "gender_mf"]}], "writes": [{"table": "farm", "columns": ["num_stops", "gender_mf"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO violations SELECT fault_description, actual_order_id, visitdate, group_equity_shareholding FROM broadband_customers_global WHERE fault_description > 261\"\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": ["fault_description", "actual_order_id", "visitdate", "group_equity_shareholding"]}], "writes": [{"table": "violations", "columns": ["fault_description", "actual_order_id", "visitdate", "group_equity_shareholding"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads_payments_daily SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wastewater_facilities\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "wastewater_facilities", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO producers SELECT prominence, decor, incident_count FROM menu WHERE prominence > 53\"\n", "labels": {"reads": [{"table": "menu", "columns": ["prominence", "decor", "incident_count"]}], "writes": [{"table": "producers", "columns": ["prominence", "decor", "incident_count"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO submarine_canyons SELECT part_id, business_size FROM airportdata WHERE part_id > 182\")\n", "labels": {"reads": [{"table": "airportdata", "columns": ["part_id", "business_size"]}], "writes": [{"table": "submarine_canyons", "columns": ["part_id", "business_size"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fans_merchandise_basketball SELECT booked_amount, pixels, policyholder_id, review_id FROM district_schools WHERE booked_amount > 330\"\n", "labels": {"reads": [{"table": "district_schools", "columns": ["booked_amount", "pixels", "policyholder_id", "review_id"]}], "writes": [{"table": "fans_merchandise_basketball", "columns": ["booked_amount", "pixels", "policyholder_id", "review_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO rigs SELECT a.sustainable, b.visit_month FROM tickets a JOIN instructors b ON a.farmer_name = b.farmer_name\"\n", "labels": {"reads": [{"table": "tickets", "columns": null}, {"table": "instructors", "columns": null}], "writes": [{"table": "rigs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT zip_postcode, zone FROM mammals LIMIT 342\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO automation_tech SELECT accommodationtype, claim_outcome_code FROM follows WHERE accommodationtype > 268\")\n", "labels": {"reads": [{"table": "mammals", "columns": ["zip_postcode", "zone"]}, {"table": "follows", "columns": ["accommodationtype", "claim_outcome_code"]}], "writes": [{"table": "automation_tech", "columns": ["accommodationtype", "claim_outcome_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO trips (store_id, artwork_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "trips", "columns": ["store_id", "artwork_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO student SELECT a.donorid, b.dispensaryid FROM reporters a JOIN bioprocesses b ON a.reaction_time = b.reaction_time\"\n", "labels": {"reads": [{"table": "reporters", "columns": null}, {"table": "bioprocesses", "columns": null}], "writes": [{"table": "student", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mining_operation_data SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"city_tech\")\ndump_to_warehouse(df, \"student_course_attendance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "city_tech", "columns": null}], "writes": [{"table": "student_course_attendance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO manager_award SELECT f_id, garment_type, strain_type FROM milestones WHERE f_id > 438\"], check=True)\n", "labels": {"reads": [{"table": "milestones", "columns": ["f_id", "garment_type", "strain_type"]}], "writes": [{"table": "manager_award", "columns": ["f_id", "garment_type", "strain_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergency_responses\").toPandas()\ndf[[\"start\", \"max_cargo_weight\"]].to_sql(\"game_results\", engine, index=False)\n", "labels": {"reads": [{"table": "emergency_responses", "columns": null}], "writes": [{"table": "game_results", "columns": ["start", "max_cargo_weight"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO department SELECT a.card_number, b.silver FROM un_peacekeeping_operations a JOIN ods_clicks_df b ON a.account_id = b.account_id\"\n", "labels": {"reads": [{"table": "un_peacekeeping_operations", "columns": null}, {"table": "ods_clicks_df", "columns": null}], "writes": [{"table": "department", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO militarydrones SELECT sellingprice, production_bopd, participation_id FROM researchgrants WHERE sellingprice > 273\")\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["sellingprice", "production_bopd", "participation_id"]}], "writes": [{"table": "militarydrones", "columns": ["sellingprice", "production_bopd", "participation_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO league SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO bikerental SELECT custid, fleet_series, avg_yield, quality_rank FROM route_fares WHERE custid > 268\")\n", "labels": {"reads": [{"table": "route_fares", "columns": ["custid", "fleet_series", "avg_yield", "quality_rank"]}], "writes": [{"table": "bikerental", "columns": ["custid", "fleet_series", "avg_yield", "quality_rank"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cultural_heritage SELECT devices, energy_efficiency_rating, start_date, tour_name FROM haircaresales WHERE devices > 172\"\n", "labels": {"reads": [{"table": "haircaresales", "columns": ["devices", "energy_efficiency_rating", "start_date", "tour_name"]}], "writes": [{"table": "cultural_heritage", "columns": ["devices", "energy_efficiency_rating", "start_date", "tour_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recalls\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "recalls", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.rig_id > 128).all()\n# src table: bi_refunds_daily\nengine.execute(\"INSERT INTO incidents SELECT * FROM bi_refunds_daily\")\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": null}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO providers SELECT meal_id, sales_channel FROM chemicalbatches WHERE meal_id > 360\"], check=True)\n", "labels": {"reads": [{"table": "chemicalbatches", "columns": ["meal_id", "sales_channel"]}], "writes": [{"table": "providers", "columns": ["meal_id", "sales_channel"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT wrestler_id, min_temperature_f FROM africa_projects\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"eu_humanitarian_assistance\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "africa_projects", "columns": ["wrestler_id", "min_temperature_f"]}], "writes": [{"table": "eu_humanitarian_assistance", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO caseattorneys SELECT investment_amount, founder_identifies_as_lgbtq, customer_name, poll_source FROM bi_refunds_daily WHERE investment_amount > 207\"], check=True)\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["investment_amount", "founder_identifies_as_lgbtq", "customer_name", "poll_source"]}], "writes": [{"table": "caseattorneys", "columns": ["investment_amount", "founder_identifies_as_lgbtq", "customer_name", "poll_source"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO member_details SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO youth_fan_participation SELECT dept_id, region_name FROM bank_info WHERE dept_id > 410\"\n", "labels": {"reads": [{"table": "bank_info", "columns": ["dept_id", "region_name"]}], "writes": [{"table": "youth_fan_participation", "columns": ["dept_id", "region_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO impact_investments SELECT num_workers, costid FROM atlantic_plate WHERE num_workers > 3\"\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": ["num_workers", "costid"]}], "writes": [{"table": "impact_investments", "columns": ["num_workers", "costid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dancefunding depends on security_incidents\ndbt run -s dancefunding --vars 'source: security_incidents'\n", "labels": {"reads": [{"table": "security_incidents", "columns": null}], "writes": [{"table": "dancefunding", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO art SELECT a.all_home, b.game_genre FROM medical_facilities_nyc a JOIN stations b ON a.personnel_id = b.personnel_id\"\n", "labels": {"reads": [{"table": "medical_facilities_nyc", "columns": null}, {"table": "stations", "columns": null}], "writes": [{"table": "art", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.funding_round_id > 419).all()\n# src table: chemical_processes\nengine.execute(\"INSERT INTO construction_labor SELECT * FROM chemical_processes\")\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": [{"table": "construction_labor", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT bandmateid, name FROM transportation_fleet LIMIT 179\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO bi.inventory_delta SELECT permit_date, feedback_id, bike_id, transaction_id FROM dwd_coupon_use_hourly WHERE permit_date > 344\")\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": ["bandmateid", "name"]}, {"table": "dwd_coupon_use_hourly", "columns": ["permit_date", "feedback_id", "bike_id", "transaction_id"]}], "writes": [{"table": "bi.inventory_delta", "columns": ["permit_date", "feedback_id", "bike_id", "transaction_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO sustainableproduction SELECT a.date_of_completion, b.vehicle_flight_number FROM guests a JOIN underwater_cables b ON a.hiredate = b.hiredate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "guests", "columns": null}, {"table": "underwater_cables", "columns": null}], "writes": [{"table": "sustainableproduction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"communityhealthworkerscanada\").toPandas()\ndf[[\"group_name\", \"gas_fee\"]].to_sql(\"iron\", engine, index=False)\n", "labels": {"reads": [{"table": "communityhealthworkerscanada", "columns": null}], "writes": [{"table": "iron", "columns": ["group_name", "gas_fee"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO associatedheritages SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"habitat\").toPandas()\ndf[[\"incorporated_in\", \"openingid\"]].to_sql(\"drills\", engine, index=False)\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "drills", "columns": ["incorporated_in", "openingid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO mart_exposure_hourly SELECT a.spending, b.completion_date FROM bus_fare_collection a JOIN vrusers b ON a.farmname = b.farmname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": null}, {"table": "vrusers", "columns": null}], "writes": [{"table": "mart_exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.inventory_hourly SELECT tree_type_id, marketing_region_name FROM warehouses WHERE tree_type_id > 487\"\n", "labels": {"reads": [{"table": "warehouses", "columns": ["tree_type_id", "marketing_region_name"]}], "writes": [{"table": "mart.inventory_hourly", "columns": ["tree_type_id", "marketing_region_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart.mart_device_log SELECT middle_name, ota_id FROM ods_risk_score_delta WHERE middle_name > 451\"\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": ["middle_name", "ota_id"]}], "writes": [{"table": "mart.mart_device_log", "columns": ["middle_name", "ota_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO aquaticfarm SELECT * FROM legacy\ncur.execute(\"SELECT projectname, team_id_br FROM smart_city_projects LIMIT 299\")\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": ["projectname", "team_id_br"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT enable_dm, hospitalname FROM cultural_events LIMIT 324\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "cultural_events", "columns": ["enable_dm", "hospitalname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO workforce_development SELECT year_founded, productionid FROM customer_contact_channels WHERE year_founded > 438\"], check=True)\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": ["year_founded", "productionid"]}], "writes": [{"table": "workforce_development", "columns": ["year_founded", "productionid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model tryout depends on investmentsesg\ndbt run --models tryout --vars '{\"source_table\":\"investmentsesg\"}'\n", "labels": {"reads": [{"table": "investmentsesg", "columns": null}], "writes": [{"table": "tryout", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT treatment_date, topic FROM ocean_salinity LIMIT 240\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO files SELECT trader_id, draft_class, donationyear, time_id FROM high_risk WHERE trader_id > 201\")\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": ["treatment_date", "topic"]}, {"table": "high_risk", "columns": ["trader_id", "draft_class", "donationyear", "time_id"]}], "writes": [{"table": "files", "columns": ["trader_id", "draft_class", "donationyear", "time_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO organic_products SELECT university_type, trainingdate, individual_last_name FROM stg.stg_events_di WHERE university_type > 493\"\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": ["university_type", "trainingdate", "individual_last_name"]}], "writes": [{"table": "organic_products", "columns": ["university_type", "trainingdate", "individual_last_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO organisations SELECT alid, hours_served, threat FROM exit_strategies WHERE alid > 69\"\n", "labels": {"reads": [{"table": "exit_strategies", "columns": ["alid", "hours_served", "threat"]}], "writes": [{"table": "organisations", "columns": ["alid", "hours_served", "threat"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.safety_id > 394).all()\n# src table: infantmortalitydata\nengine.execute(\"INSERT INTO crops SELECT * FROM infantmortalitydata\")\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": null}], "writes": [{"table": "crops", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO participation SELECT pieces, document_status_description FROM restorative_justice_programs WHERE pieces > 271\")\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": ["pieces", "document_status_description"]}], "writes": [{"table": "participation", "columns": ["pieces", "document_status_description"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO market_access SELECT financing_date, billing_state, crop_id FROM dws_clicks_di WHERE financing_date > 20\"\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": ["financing_date", "billing_state", "crop_id"]}], "writes": [{"table": "market_access", "columns": ["financing_date", "billing_state", "crop_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"appellations\")\npush_to_output(df, \"housing_investments\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "appellations", "columns": null}], "writes": [{"table": "housing_investments", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workout\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw.shipments_df\")\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": [{"table": "dw.shipments_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vehicles (releaseyear, treatment_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vehicles", "columns": ["releaseyear", "treatment_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT area_sqkm, num_owners FROM rural_economy_2 LIMIT 97\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO safetytests SELECT lastclaimdate, field, cinema_id FROM states WHERE lastclaimdate > 453\")\n", "labels": {"reads": [{"table": "rural_economy_2", "columns": ["area_sqkm", "num_owners"]}, {"table": "states", "columns": ["lastclaimdate", "field", "cinema_id"]}], "writes": [{"table": "safetytests", "columns": ["lastclaimdate", "field", "cinema_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cargo_equipment SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"mart.mart_events_di\")\nexport_to_target(df, \"cases\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_events_di", "columns": null}], "writes": [{"table": "cases", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT phone, workoutdate FROM healthcare_budget LIMIT 352\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO artist SELECT max_temperature_f, wins, valuation FROM platform WHERE max_temperature_f > 159\")\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": ["phone", "workoutdate"]}, {"table": "platform", "columns": ["max_temperature_f", "wins", "valuation"]}], "writes": [{"table": "artist", "columns": ["max_temperature_f", "wins", "valuation"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO labels SELECT founder_veteran, incident_type_description, project_date, genrename FROM workout WHERE founder_veteran > 436\"\n", "labels": {"reads": [{"table": "workout", "columns": ["founder_veteran", "incident_type_description", "project_date", "genrename"]}], "writes": [{"table": "labels", "columns": ["founder_veteran", "incident_type_description", "project_date", "genrename"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cotton_source --columns bioprocess_name,permits_issued --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cotton_source", "columns": ["bioprocess_name", "permits_issued"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"concert_revenue\")\nupsert_to_output(df, \"peacekeepingmissions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "concert_revenue", "columns": null}], "writes": [{"table": "peacekeepingmissions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO wellbeing_programs SELECT rural_area, consumption, group_id FROM therapy_attendance WHERE rural_area > 274\"\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": ["rural_area", "consumption", "group_id"]}], "writes": [{"table": "wellbeing_programs", "columns": ["rural_area", "consumption", "group_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"construction_labor_stats\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"vessel_incident_count\")\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": null}], "writes": [{"table": "vessel_incident_count", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads_exposure_hourly SELECT mission_name, pallet_id, unit, owner FROM bi.bi_payments WHERE mission_name > 255;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_payments", "columns": ["mission_name", "pallet_id", "unit", "owner"]}], "writes": [{"table": "ads_exposure_hourly", "columns": ["mission_name", "pallet_id", "unit", "owner"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.customer > 4).all()\n# src table: baseball_teams\nengine.execute(\"INSERT INTO ads.ads_risk_score_hourly SELECT * FROM baseball_teams\")\n", "labels": {"reads": [{"table": "baseball_teams", "columns": null}], "writes": [{"table": "ads.ads_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart.mart_member_point_hourly SELECT facid, athlete FROM arctictemperature WHERE facid > 149\"\n", "labels": {"reads": [{"table": "arctictemperature", "columns": ["facid", "athlete"]}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": ["facid", "athlete"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO support SELECT loadingend, drug, book_club_id FROM mart.risk_score_df WHERE loadingend > 105\")\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["loadingend", "drug", "book_club_id"]}], "writes": [{"table": "support", "columns": ["loadingend", "drug", "book_club_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO artistsales SELECT date_claim_made, framework_id, casestatus FROM crimes WHERE date_claim_made > 339\")\n", "labels": {"reads": [{"table": "crimes", "columns": ["date_claim_made", "framework_id", "casestatus"]}], "writes": [{"table": "artistsales", "columns": ["date_claim_made", "framework_id", "casestatus"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"lead_mines\").toPandas()\ndf[[\"surname\", \"inventor_name\"]].to_sql(\"green_energy_lending_programs\", engine, index=False)\n", "labels": {"reads": [{"table": "lead_mines", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": ["surname", "inventor_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.risk_score_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"vessel_tracking\")\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": null}], "writes": [{"table": "vessel_tracking", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO apartment_facilities SELECT shipping_mode, virtual_tour_views, sex FROM engineer_skills WHERE shipping_mode > 253\"\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["shipping_mode", "virtual_tour_views", "sex"]}], "writes": [{"table": "apartment_facilities", "columns": ["shipping_mode", "virtual_tour_views", "sex"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO restaurant_type SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mart.mart_device_log_hourly SELECT transact_count, shariah_compliant_investment_amount FROM experience WHERE transact_count > 268;\nEOF\n", "labels": {"reads": [{"table": "experience", "columns": ["transact_count", "shariah_compliant_investment_amount"]}], "writes": [{"table": "mart.mart_device_log_hourly", "columns": ["transact_count", "shariah_compliant_investment_amount"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO soccer_teams SELECT * FROM legacy\ncur.execute(\"SELECT galleryname, date_stored FROM investor_activities LIMIT 455\")\n", "labels": {"reads": [{"table": "investor_activities", "columns": ["galleryname", "date_stored"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT monthly_rental, total_cost FROM imagery_archive LIMIT 337\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "imagery_archive", "columns": ["monthly_rental", "total_cost"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"invoice\")\nsrc.write.insertInto(\"drivers\", overwrite=True)\n", "labels": {"reads": [{"table": "invoice", "columns": null}], "writes": [{"table": "drivers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"traffic_violations\")\nsrc.write.insertInto(\"government.region\", overwrite=True)\n", "labels": {"reads": [{"table": "traffic_violations", "columns": null}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model video_content depends on entrepreneur\ndbt build --select video_content --vars 'source: entrepreneur'\n", "labels": {"reads": [{"table": "entrepreneur", "columns": null}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws_cart_item\", conn)\ndf.to_sql(\"wastewater_treatment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": [{"table": "wastewater_treatment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO habitat_preservation SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ma_inspections\"\n", "labels": {"reads": [{"table": "ma_inspections", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO program_budget SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO member_data SELECT a.assets_billion, b.count FROM athlete_wellbeing a JOIN factories b ON a.coach_name = b.coach_name\"\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": null}, {"table": "factories", "columns": null}], "writes": [{"table": "member_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"regular_order_products\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"trainmaintenance\")\n", "labels": {"reads": [{"table": "regular_order_products", "columns": null}], "writes": [{"table": "trainmaintenance", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO incident_region (acc_type, discovery_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "incident_region", "columns": ["acc_type", "discovery_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"departments\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads_payments_hourly\")\n", "labels": {"reads": [{"table": "departments", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT lot_details, order_item_status FROM dws.dws_users_hourly\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"operate_company\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_users_hourly", "columns": ["lot_details", "order_item_status"]}], "writes": [{"table": "operate_company", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sponsor_trials SELECT mappingid, co2_reduction FROM operations WHERE mappingid > 49;\nEOF\n", "labels": {"reads": [{"table": "operations", "columns": ["mappingid", "co2_reduction"]}], "writes": [{"table": "sponsor_trials", "columns": ["mappingid", "co2_reduction"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO distributors SELECT organisation_id, ship_name, exhibit_location FROM regulatory_frameworks WHERE organisation_id > 68\"\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": ["organisation_id", "ship_name", "exhibit_location"]}], "writes": [{"table": "distributors", "columns": ["organisation_id", "ship_name", "exhibit_location"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT menuitemid, policy FROM community_health_centers\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"rd_expenditure\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "community_health_centers", "columns": ["menuitemid", "policy"]}], "writes": [{"table": "rd_expenditure", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO parts SELECT * FROM legacy\ncur.execute(\"SELECT rec_engine, workouttype FROM match LIMIT 333\")\n", "labels": {"reads": [{"table": "match", "columns": ["rec_engine", "workouttype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table community_health_workers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "community_health_workers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tv_shows_genre SELECT wage_increase, architect_id, effort_name, mealname FROM organic_farms WHERE wage_increase > 21\"\n", "labels": {"reads": [{"table": "organic_farms", "columns": ["wage_increase", "architect_id", "effort_name", "mealname"]}], "writes": [{"table": "tv_shows_genre", "columns": ["wage_increase", "architect_id", "effort_name", "mealname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO team_franchise SELECT a.date_in_locaton_to, b.courses FROM apac_hotel_views a JOIN salesdata b ON a.carriername = b.carriername\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": null}, {"table": "salesdata", "columns": null}], "writes": [{"table": "team_franchise", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO satellite_deployment SELECT * FROM legacy\ncur.execute(\"SELECT head_id, billid FROM carbon_pricing LIMIT 64\")\n", "labels": {"reads": [{"table": "carbon_pricing", "columns": ["head_id", "billid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"financial_capability_program\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"community_engagement\")\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": null}], "writes": [{"table": "community_engagement", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO renewabletypes SELECT virtual_tour_engagement_time, entrydate, dockingid FROM exhibitionattendance WHERE virtual_tour_engagement_time > 315\")\n", "labels": {"reads": [{"table": "exhibitionattendance", "columns": ["virtual_tour_engagement_time", "entrydate", "dockingid"]}], "writes": [{"table": "renewabletypes", "columns": ["virtual_tour_engagement_time", "entrydate", "dockingid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_areas\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"authors\")\n", "labels": {"reads": [{"table": "rural_areas", "columns": null}], "writes": [{"table": "authors", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO volunteerprograms SELECT nominee, donor_category, artistname FROM sustainable_projects WHERE nominee > 12\")\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["nominee", "donor_category", "artistname"]}], "writes": [{"table": "volunteerprograms", "columns": ["nominee", "donor_category", "artistname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO carbon_pricing (production_qty, interest_group) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "carbon_pricing", "columns": ["production_qty", "interest_group"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO carbon_prices SELECT gender_group, bikes_available, college FROM submission WHERE gender_group > 88\")\n", "labels": {"reads": [{"table": "submission", "columns": ["gender_group", "bikes_available", "college"]}], "writes": [{"table": "carbon_prices", "columns": ["gender_group", "bikes_available", "college"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO drought_data SELECT total_attendance, yield_per_acre FROM user_reactions WHERE total_attendance > 245;\nEOF\n", "labels": {"reads": [{"table": "user_reactions", "columns": ["total_attendance", "yield_per_acre"]}], "writes": [{"table": "drought_data", "columns": ["total_attendance", "yield_per_acre"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ucl_top10 SELECT * FROM legacy\ncur.execute(\"SELECT granteeid, team_name FROM virtual_tour_offers LIMIT 77\")\n", "labels": {"reads": [{"table": "virtual_tour_offers", "columns": ["granteeid", "team_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM teachers\", conn)\ndf.to_sql(\"culturalcompetency\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "teachers", "columns": null}], "writes": [{"table": "culturalcompetency", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO player_coach (strainname, serviceid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "player_coach", "columns": ["strainname", "serviceid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO contract_states (name_last, orderdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "contract_states", "columns": ["name_last", "orderdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO provider_training SELECT request_id, type, opname FROM dw_member_point_full WHERE request_id > 415\"\n", "labels": {"reads": [{"table": "dw_member_point_full", "columns": ["request_id", "type", "opname"]}], "writes": [{"table": "provider_training", "columns": ["request_id", "type", "opname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO jupiter_spacecraft (building_manager, town_city) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "jupiter_spacecraft", "columns": ["building_manager", "town_city"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT delivery_date, latitude FROM researchpapers\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"part_faults\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "researchpapers", "columns": ["delivery_date", "latitude"]}], "writes": [{"table": "part_faults", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ocean_depths SELECT * FROM legacy\ncur.execute(\"SELECT treatment_type, donor_state FROM region LIMIT 171\")\n", "labels": {"reads": [{"table": "region", "columns": ["treatment_type", "donor_state"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM energy_prices\"\n", "labels": {"reads": [{"table": "energy_prices", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"has_amenity\").toPandas()\ndf[[\"impact_score\", \"investment_amount\"]].to_sql(\"manufacturingplants\", engine, index=False)\n", "labels": {"reads": [{"table": "has_amenity", "columns": null}], "writes": [{"table": "manufacturingplants", "columns": ["impact_score", "investment_amount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO survey_data (building_description, job_category) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "survey_data", "columns": ["building_description", "job_category"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO city_labor_cost SELECT shop_id, total_points, room FROM customer_transactions WHERE shop_id > 189\"], check=True)\n", "labels": {"reads": [{"table": "customer_transactions", "columns": ["shop_id", "total_points", "room"]}], "writes": [{"table": "city_labor_cost", "columns": ["shop_id", "total_points", "room"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT is_accessible, menu_type FROM fertilizer LIMIT 372\")\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO organic_farms SELECT active, dapp_name, curator, pass_fail FROM innovation_projects WHERE active > 72\")\n", "labels": {"reads": [{"table": "fertilizer", "columns": ["is_accessible", "menu_type"]}, {"table": "innovation_projects", "columns": ["active", "dapp_name", "curator", "pass_fail"]}], "writes": [{"table": "organic_farms", "columns": ["active", "dapp_name", "curator", "pass_fail"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO waste_generation_city_v2 SELECT * FROM legacy\ncur.execute(\"SELECT vrgameid, product_category FROM genetic.projects LIMIT 487\")\n", "labels": {"reads": [{"table": "genetic.projects", "columns": ["vrgameid", "product_category"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table show --target-dir /tmp/land\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_video_view\").toPandas()\ndf[[\"awards\", \"eia_date\"]].to_sql(\"casebilling\", engine, index=False)\n", "labels": {"reads": [{"table": "user_video_view", "columns": null}], "writes": [{"table": "casebilling", "columns": ["awards", "eia_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_exposure_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"student_addresses\")\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": null}], "writes": [{"table": "student_addresses", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO program_budget SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table veteran_unemployment --columns trainingname,certification_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "veteran_unemployment", "columns": ["trainingname", "certification_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO follows SELECT feb, beds, cuisine_id FROM bi.bi_events_daily WHERE feb > 265\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["feb", "beds", "cuisine_id"]}], "writes": [{"table": "follows", "columns": ["feb", "beds", "cuisine_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"online_platform\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"state_usage\")\n", "labels": {"reads": [{"table": "online_platform", "columns": null}], "writes": [{"table": "state_usage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.inventory_df SELECT * FROM legacy\ncur.execute(\"SELECT crop, fundingdate FROM supply_chain LIMIT 283\")\n", "labels": {"reads": [{"table": "supply_chain", "columns": ["crop", "fundingdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.dwd_campaigns_df --columns last_updated,volunteer_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": ["last_updated", "volunteer_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO humanitarian_operations SELECT program_date, storename, thing_id FROM space_exploration WHERE program_date > 85;\nEOF\n", "labels": {"reads": [{"table": "space_exploration", "columns": ["program_date", "storename", "thing_id"]}], "writes": [{"table": "humanitarian_operations", "columns": ["program_date", "storename", "thing_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM district\"\n", "labels": {"reads": [{"table": "district", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model time_dim depends on emergencyservices\ndbt run -s time_dim --vars '{\"src\":\"emergencyservices\"}'\n", "labels": {"reads": [{"table": "emergencyservices", "columns": null}], "writes": [{"table": "time_dim", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO hydro_power SELECT vehicle_id, socialimpactscore, fundingid FROM urban_initiatives WHERE vehicle_id > 72\")\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": ["vehicle_id", "socialimpactscore", "fundingid"]}], "writes": [{"table": "hydro_power", "columns": ["vehicle_id", "socialimpactscore", "fundingid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO innovation_trends SELECT a.providerid, b.team_name FROM wells a JOIN savings b ON a.maxoccupancy = b.maxoccupancy\"\n", "labels": {"reads": [{"table": "wells", "columns": null}, {"table": "savings", "columns": null}], "writes": [{"table": "innovation_trends", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ocean_floor_mapping SELECT regionid, width, attendee_race, address_road FROM investors WHERE regionid > 225;\nEOF\n", "labels": {"reads": [{"table": "investors", "columns": ["regionid", "width", "attendee_race", "address_road"]}], "writes": [{"table": "ocean_floor_mapping", "columns": ["regionid", "width", "attendee_race", "address_road"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO new_schedules SELECT time_day, year_built FROM ocean_shipping.cargo WHERE time_day > 193\"\n", "labels": {"reads": [{"table": "ocean_shipping.cargo", "columns": ["time_day", "year_built"]}], "writes": [{"table": "new_schedules", "columns": ["time_day", "year_built"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.clicks_di (trip_distance, ticketprice) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.clicks_di", "columns": ["trip_distance", "ticketprice"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"stats\")\npersist_to_sink(df, \"investors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stats", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_orders_daily SELECT a.player_name, b.health_equity_metric_1 FROM tourist_destinations a JOIN budget b ON a.delivery_date = b.delivery_date\"\n", "labels": {"reads": [{"table": "tourist_destinations", "columns": null}, {"table": "budget", "columns": null}], "writes": [{"table": "bi.bi_orders_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO customer_month SELECT name, billingcountry FROM transportation_per_country WHERE name > 182;\nEOF\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": ["name", "billingcountry"]}], "writes": [{"table": "customer_month", "columns": ["name", "billingcountry"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT functional_area_description, artistid FROM candidate_assessments LIMIT 102;\nINSERT INTO policy SELECT customer_id, date_contact_to, destination_name, price_in_euros FROM florida_conservation_initiatives WHERE customer_id > 148;\nSQL\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": ["functional_area_description", "artistid"]}, {"table": "florida_conservation_initiatives", "columns": ["customer_id", "date_contact_to", "destination_name", "price_in_euros"]}], "writes": [{"table": "policy", "columns": ["customer_id", "date_contact_to", "destination_name", "price_in_euros"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO cargo_data SELECT resolved, date_of_latest_revision, project_category FROM vulnerabilities WHERE resolved > 144;\nEOF\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": ["resolved", "date_of_latest_revision", "project_category"]}], "writes": [{"table": "cargo_data", "columns": ["resolved", "date_of_latest_revision", "project_category"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO military_personnel_africa SELECT a.adults, b.claim_id FROM review a JOIN people_addresses b ON a.mar = b.mar\"\n", "labels": {"reads": [{"table": "review", "columns": null}, {"table": "people_addresses", "columns": null}], "writes": [{"table": "military_personnel_africa", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"southeast_providers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mentalhealthprovider\")\n", "labels": {"reads": [{"table": "southeast_providers", "columns": null}], "writes": [{"table": "mentalhealthprovider", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_offsets SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO climate_data SELECT technique_id, amount_payment, union_members, member_in_charge_id FROM traffic_violations WHERE technique_id > 203\"\n", "labels": {"reads": [{"table": "traffic_violations", "columns": ["technique_id", "amount_payment", "union_members", "member_in_charge_id"]}], "writes": [{"table": "climate_data", "columns": ["technique_id", "amount_payment", "union_members", "member_in_charge_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"routes\")\nsave_to_sink(df, \"ocean_species\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "routes", "columns": null}], "writes": [{"table": "ocean_species", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO space_programs SELECT * FROM legacy\ncur.execute(\"SELECT cuisine, accessible FROM document_sections_images LIMIT 360\")\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["cuisine", "accessible"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO playerscores (device_name, date_claim_settled) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "playerscores", "columns": ["device_name", "date_claim_settled"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT label_id, change_date FROM flu_shots\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"sector_incidents\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "flu_shots", "columns": ["label_id", "change_date"]}], "writes": [{"table": "sector_incidents", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT area_sqkm, shipment_id FROM waste_generation LIMIT 381\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO trips SELECT farmland_id, framework_name FROM posts WHERE farmland_id > 331\")\n", "labels": {"reads": [{"table": "waste_generation", "columns": ["area_sqkm", "shipment_id"]}, {"table": "posts", "columns": ["farmland_id", "framework_name"]}], "writes": [{"table": "trips", "columns": ["farmland_id", "framework_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.worker > 432).all()\n# src table: permian_basin\nengine.execute(\"INSERT INTO model_fairness SELECT * FROM permian_basin\")\n", "labels": {"reads": [{"table": "permian_basin", "columns": null}], "writes": [{"table": "model_fairness", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investments\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"city_department\")\n", "labels": {"reads": [{"table": "investments", "columns": null}], "writes": [{"table": "city_department", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT productionrate, commodity FROM customer_policies LIMIT 404;\nINSERT INTO autonomousvehicleaccidents SELECT time_of_purchase, registration_id, building_short_name FROM artworksales WHERE time_of_purchase > 86;\nSQL\n", "labels": {"reads": [{"table": "customer_policies", "columns": ["productionrate", "commodity"]}, {"table": "artworksales", "columns": ["time_of_purchase", "registration_id", "building_short_name"]}], "writes": [{"table": "autonomousvehicleaccidents", "columns": ["time_of_purchase", "registration_id", "building_short_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO phone SELECT visitdate, steps, origin FROM strandings WHERE visitdate > 152\")\n", "labels": {"reads": [{"table": "strandings", "columns": ["visitdate", "steps", "origin"]}], "writes": [{"table": "phone", "columns": ["visitdate", "steps", "origin"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"donationsbycause\")\nsrc.write.insertInto(\"mart_shipments_full\", overwrite=True)\n", "labels": {"reads": [{"table": "donationsbycause", "columns": null}], "writes": [{"table": "mart_shipments_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bike_stations depends on marine_species_indian\ndbt build --models bike_stations --vars '{\"source_table\":\"marine_species_indian\"}'\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": null}], "writes": [{"table": "bike_stations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"highways\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "highways", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO therapists SELECT * FROM legacy\ncur.execute(\"SELECT extraction_amount, staff_first_name FROM dws.inventory_df LIMIT 251\")\n", "labels": {"reads": [{"table": "dws.inventory_df", "columns": ["extraction_amount", "staff_first_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO makeup_products SELECT lettergrade, borough, outcome_code, item_sold FROM ads.vendors_delta WHERE lettergrade > 119\"\n", "labels": {"reads": [{"table": "ads.vendors_delta", "columns": ["lettergrade", "borough", "outcome_code", "item_sold"]}], "writes": [{"table": "makeup_products", "columns": ["lettergrade", "borough", "outcome_code", "item_sold"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"member_of_club\")\ndump_to_store(df, \"league_x\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "member_of_club", "columns": null}], "writes": [{"table": "league_x", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO stg.stg_exposure_di SELECT exhibitionname, bill_id FROM caseattorneys WHERE exhibitionname > 472\")\n", "labels": {"reads": [{"table": "caseattorneys", "columns": ["exhibitionname", "bill_id"]}], "writes": [{"table": "stg.stg_exposure_di", "columns": ["exhibitionname", "bill_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dysprosiumproduction SELECT text_of_notes, ticket_price, co2_reduction_tons FROM hair_care_sales WHERE text_of_notes > 471\"\n", "labels": {"reads": [{"table": "hair_care_sales", "columns": ["text_of_notes", "ticket_price", "co2_reduction_tons"]}], "writes": [{"table": "dysprosiumproduction", "columns": ["text_of_notes", "ticket_price", "co2_reduction_tons"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table exit_strategies --columns port_code,customer_number --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "exit_strategies", "columns": ["port_code", "customer_number"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"reservations\").toPandas()\ndf[[\"portfolio_id\", \"quality\"]].to_sql(\"deep_sea_expeditions\", engine, index=False)\n", "labels": {"reads": [{"table": "reservations", "columns": null}], "writes": [{"table": "deep_sea_expeditions", "columns": ["portfolio_id", "quality"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tournaments SELECT style, shipping_mode, hourid FROM video_content WHERE style > 460\"\n", "labels": {"reads": [{"table": "video_content", "columns": ["style", "shipping_mode", "hourid"]}], "writes": [{"table": "tournaments", "columns": ["style", "shipping_mode", "hourid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT market, mediatypeid FROM space_missions LIMIT 78;\nINSERT INTO scan_dates SELECT requestdate, athlete, operationdate, budget_type_description FROM document_sections_images WHERE requestdate > 318;\nSQL\n", "labels": {"reads": [{"table": "space_missions", "columns": ["market", "mediatypeid"]}, {"table": "document_sections_images", "columns": ["requestdate", "athlete", "operationdate", "budget_type_description"]}], "writes": [{"table": "scan_dates", "columns": ["requestdate", "athlete", "operationdate", "budget_type_description"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO teaches SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fairtradecertification SELECT number_of_platforms, bus_id, date_valid_to, communityid FROM org_volunteer WHERE number_of_platforms > 155\"\n", "labels": {"reads": [{"table": "org_volunteer", "columns": ["number_of_platforms", "bus_id", "date_valid_to", "communityid"]}], "writes": [{"table": "fairtradecertification", "columns": ["number_of_platforms", "bus_id", "date_valid_to", "communityid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO container_receipts SELECT a.therapy_sessions, b.dispensary_id FROM ads.ads_payments_delta a JOIN autoshow b ON a.underrepresented_community = b.underrepresented_community\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.ads_payments_delta", "columns": null}, {"table": "autoshow", "columns": null}], "writes": [{"table": "container_receipts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.device_log_hourly (improvement, athlete_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.device_log_hourly", "columns": ["improvement", "athlete_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table city_labor_cost --target-dir /tmp/land\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO gamesessions SELECT staff_id, payment_method_code, water_consumption, detection_date FROM eia_schedule WHERE staff_id > 266\"\n", "labels": {"reads": [{"table": "eia_schedule", "columns": ["staff_id", "payment_method_code", "water_consumption", "detection_date"]}], "writes": [{"table": "gamesessions", "columns": ["staff_id", "payment_method_code", "water_consumption", "detection_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO music_database SELECT culturalcompetency, hotel_chain_id FROM climate_investments WHERE culturalcompetency > 86;\nEOF\n", "labels": {"reads": [{"table": "climate_investments", "columns": ["culturalcompetency", "hotel_chain_id"]}], "writes": [{"table": "music_database", "columns": ["culturalcompetency", "hotel_chain_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO feedback SELECT incident, menucategory, socially_responsible, year_opened FROM round WHERE incident > 475\"\n", "labels": {"reads": [{"table": "round", "columns": ["incident", "menucategory", "socially_responsible", "year_opened"]}], "writes": [{"table": "feedback", "columns": ["incident", "menucategory", "socially_responsible", "year_opened"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table state_budget --columns menu_id,max_temperature_f --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "state_budget", "columns": ["menu_id", "max_temperature_f"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.device_log\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.mart_coupon_use_delta\")\n", "labels": {"reads": [{"table": "bi.device_log", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table contracts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "contracts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT transact_date, property_price FROM equipment_maintenance LIMIT 485\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": ["transact_date", "property_price"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods_member_point_full\"\n", "labels": {"reads": [{"table": "ods_member_point_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT citizen_id, price FROM vehicle_registrations LIMIT 200\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": ["citizen_id", "price"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tree_habitat_associations SELECT recruiterid, bridgeid, commission_pct FROM digital_assets WHERE recruiterid > 353\"], check=True)\n", "labels": {"reads": [{"table": "digital_assets", "columns": ["recruiterid", "bridgeid", "commission_pct"]}], "writes": [{"table": "tree_habitat_associations", "columns": ["recruiterid", "bridgeid", "commission_pct"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO russia_nato_diplomacy SELECT video_id, quantity_containers, team_name FROM climate_adaptation_re WHERE video_id > 454;\nEOF\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": ["video_id", "quantity_containers", "team_name"]}], "writes": [{"table": "russia_nato_diplomacy", "columns": ["video_id", "quantity_containers", "team_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model volunteer_signups depends on shared_rides_tokyo\ndbt run -s volunteer_signups --vars '{\"source_table\":\"shared_rides_tokyo\"}'\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": null}], "writes": [{"table": "volunteer_signups", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"tunnels\")\nsrc.write.insertInto(\"venture\", overwrite=True)\n", "labels": {"reads": [{"table": "tunnels", "columns": null}], "writes": [{"table": "venture", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT center, model_id FROM performances LIMIT 26\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "performances", "columns": ["center", "model_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO product_ingredient SELECT vaccination_status, union_member FROM charging_stations WHERE vaccination_status > 56\"], check=True)\n", "labels": {"reads": [{"table": "charging_stations", "columns": ["vaccination_status", "union_member"]}], "writes": [{"table": "product_ingredient", "columns": ["vaccination_status", "union_member"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO rigs SELECT * FROM legacy\ncur.execute(\"SELECT fish_id, count_id FROM readership LIMIT 299\")\n", "labels": {"reads": [{"table": "readership", "columns": ["fish_id", "count_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"auto_show\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "auto_show", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_inventory_delta (white, membership_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory_delta", "columns": ["white", "membership_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model train_station depends on city_tech\ndbt build -s train_station --vars 'source: city_tech'\n", "labels": {"reads": [{"table": "city_tech", "columns": null}], "writes": [{"table": "train_station", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO electricvehiclestats SELECT a.production_bopd, b.launch_year FROM tech_accessibility_funding a JOIN researchpapers b ON a.count_date = b.count_date\"\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": null}, {"table": "researchpapers", "columns": null}], "writes": [{"table": "electricvehiclestats", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"coal\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"infrastructureprojects\")\n", "labels": {"reads": [{"table": "coal", "columns": null}], "writes": [{"table": "infrastructureprojects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO climatefinance SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"production_data\")\nsrc.write.insertInto(\"guests\", overwrite=True)\n", "labels": {"reads": [{"table": "production_data", "columns": null}], "writes": [{"table": "guests", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cargo_equipment SELECT profession_count, organic_ingredients_percentage, effort_id FROM council_tax WHERE profession_count > 123\"\n", "labels": {"reads": [{"table": "council_tax", "columns": ["profession_count", "organic_ingredients_percentage", "effort_id"]}], "writes": [{"table": "cargo_equipment", "columns": ["profession_count", "organic_ingredients_percentage", "effort_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO safetyincidents SELECT awayteamid, expedition_name FROM maintenance_schedule WHERE awayteamid > 287\"\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": ["awayteamid", "expedition_name"]}], "writes": [{"table": "safetyincidents", "columns": ["awayteamid", "expedition_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO brand_info SELECT * FROM legacy\ncur.execute(\"SELECT organic, attendees FROM dams LIMIT 123\")\n", "labels": {"reads": [{"table": "dams", "columns": ["organic", "attendees"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table manufacturermaterials --columns issue,section_title --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "manufacturermaterials", "columns": ["issue", "section_title"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ancient_cultures\", conn)\ndf.to_sql(\"climate_monitoring_stations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ancient_cultures", "columns": null}], "writes": [{"table": "climate_monitoring_stations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table workshops --target-dir /tmp/land\n", "labels": {"reads": [{"table": "workshops", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"affiliated_with\").toPandas()\ndf[[\"membergender\", \"date_assigned_from\"]].to_sql(\"public.crime_types\", engine, index=False)\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": [{"table": "public.crime_types", "columns": ["membergender", "date_assigned_from"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM diversion_programs\", conn)\ndf.to_sql(\"researchers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "diversion_programs", "columns": null}], "writes": [{"table": "researchers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM invoice_lines\"\n", "labels": {"reads": [{"table": "invoice_lines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ref_product_categories\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ref_product_categories", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mart.mart_coupon_use_delta\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO people_addresses SELECT date_to, transit_passengers FROM chemicals_annual WHERE date_to > 154\"\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": ["date_to", "transit_passengers"]}], "writes": [{"table": "people_addresses", "columns": ["date_to", "transit_passengers"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"ads.payments_di\")\nupsert_to_target(df, \"humanitarian_aid\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.payments_di", "columns": null}], "writes": [{"table": "humanitarian_aid", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mart_products_hourly depends on graduates\ndbt build --models mart_products_hourly --vars 'source: graduates'\n", "labels": {"reads": [{"table": "graduates", "columns": null}], "writes": [{"table": "mart_products_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.event_type_id > 490).all()\n# src table: stg.coupon_use\nengine.execute(\"INSERT INTO infrastructurebudget SELECT * FROM stg.coupon_use\")\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": null}], "writes": [{"table": "infrastructurebudget", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO members SELECT mine_name, destination_state, discovered_date FROM dws.dws_member_point_df WHERE mine_name > 329\"], check=True)\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": ["mine_name", "destination_state", "discovered_date"]}], "writes": [{"table": "members", "columns": ["mine_name", "destination_state", "discovered_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM philadelphia_police_emergencies\"\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO program_budget SELECT poll_source, ai_adoption_date, visitor_country, plantlocation FROM provinces WHERE poll_source > 161\"\n", "labels": {"reads": [{"table": "provinces", "columns": ["poll_source", "ai_adoption_date", "visitor_country", "plantlocation"]}], "writes": [{"table": "program_budget", "columns": ["poll_source", "ai_adoption_date", "visitor_country", "plantlocation"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO satellites SELECT film_id, document_type_name FROM ods.ods_events_daily WHERE film_id > 231\")\n", "labels": {"reads": [{"table": "ods.ods_events_daily", "columns": ["film_id", "document_type_name"]}], "writes": [{"table": "satellites", "columns": ["film_id", "document_type_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"material\").toPandas()\ndf[[\"coownerid\", \"green_building_id\"]].to_sql(\"coralreefs\", engine, index=False)\n", "labels": {"reads": [{"table": "material", "columns": null}], "writes": [{"table": "coralreefs", "columns": ["coownerid", "green_building_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO genetics.projects (rate, max_aperture) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "genetics.projects", "columns": ["rate", "max_aperture"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fundings\", conn)\ndf.to_sql(\"stores\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fundings", "columns": null}], "writes": [{"table": "stores", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO all_programs SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO disaster_response SELECT num_volunteers, detention_summary, cases_count FROM timed_status_of_things WHERE num_volunteers > 475\"\n", "labels": {"reads": [{"table": "timed_status_of_things", "columns": ["num_volunteers", "detention_summary", "cases_count"]}], "writes": [{"table": "disaster_response", "columns": ["num_volunteers", "detention_summary", "cases_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table stateinfrastructure --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO winter_olympics SELECT a.is_accessible, b.starting_year FROM user_ad_interactions a JOIN mart.mart_vendors b ON a.guest_first_name = b.guest_first_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": null}, {"table": "mart.mart_vendors", "columns": null}], "writes": [{"table": "winter_olympics", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO initiatives_3 (policyholderid, graduate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "initiatives_3", "columns": ["policyholderid", "graduate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dishes\").toPandas()\ndf[[\"cause_id\", \"certification\"]].to_sql(\"sustainableprojects\", engine, index=False)\n", "labels": {"reads": [{"table": "dishes", "columns": null}], "writes": [{"table": "sustainableprojects", "columns": ["cause_id", "certification"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ai_safety --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ai_safety", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO public_transportation_routes SELECT 1\"\nlogger.info(msg)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO climate_adaptation SELECT playerregion, advisor, mappingname FROM public.trips_by_day_train WHERE playerregion > 447\"\n", "labels": {"reads": [{"table": "public.trips_by_day_train", "columns": ["playerregion", "advisor", "mappingname"]}], "writes": [{"table": "climate_adaptation", "columns": ["playerregion", "advisor", "mappingname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"assessment_notes\")\nsink_to_output(df, \"ods.ods_coupon_use_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "assessment_notes", "columns": null}], "writes": [{"table": "ods.ods_coupon_use_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"site\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads_users_hourly\")\n", "labels": {"reads": [{"table": "site", "columns": null}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wind_turbines --columns lastname,openingid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wind_turbines", "columns": ["lastname", "openingid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO dwd_payments_delta SELECT a.contributiondate, b.vendor_id FROM indigenouscommunities a JOIN part_faults b ON a.clean_jerk = b.clean_jerk\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "indigenouscommunities", "columns": null}, {"table": "part_faults", "columns": null}], "writes": [{"table": "dwd_payments_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO customer_addresses SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table florida_conservation_initiatives --target-dir /tmp/land\n", "labels": {"reads": [{"table": "florida_conservation_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO inclusive_housing SELECT total_amount, price_in_dollars, albumname FROM clothingitems WHERE total_amount > 390\"], check=True)\n", "labels": {"reads": [{"table": "clothingitems", "columns": ["total_amount", "price_in_dollars", "albumname"]}], "writes": [{"table": "inclusive_housing", "columns": ["total_amount", "price_in_dollars", "albumname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table safety_violations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "safety_violations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hall_of_fame\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hall_of_fame", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shipment_id, prof_office FROM stock LIMIT 398\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "stock", "columns": ["shipment_id", "prof_office"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT restorative_justice, stat_id FROM therapy_session\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"genetics.crispr\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "therapy_session", "columns": ["restorative_justice", "stat_id"]}], "writes": [{"table": "genetics.crispr", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM document_types\"\n", "labels": {"reads": [{"table": "document_types", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT medication, servicename FROM aircraft_flights\", engine)\nimport logging\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"design_standards\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "aircraft_flights", "columns": ["medication", "servicename"]}], "writes": [{"table": "design_standards", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT college_id, ship_agent_id FROM submersible_dives LIMIT 453\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "submersible_dives", "columns": ["college_id", "ship_agent_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO factory_connections SELECT plantlocation, race_ethnicity_id, filingdate FROM ytterbium_supply WHERE plantlocation > 471\"\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": ["plantlocation", "race_ethnicity_id", "filingdate"]}], "writes": [{"table": "factory_connections", "columns": ["plantlocation", "race_ethnicity_id", "filingdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"competition\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"all_documents\")\n", "labels": {"reads": [{"table": "competition", "columns": null}], "writes": [{"table": "all_documents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.dwd_payments (invoice_date, service_type_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_payments", "columns": ["invoice_date", "service_type_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO exhibition SELECT system_id, roomname, winery, chw_id FROM incarcerated WHERE system_id > 322\"\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["system_id", "roomname", "winery", "chw_id"]}], "writes": [{"table": "exhibition", "columns": ["system_id", "roomname", "winery", "chw_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tournaments SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.missions > 175).all()\n# src table: submission\nengine.execute(\"INSERT INTO wastegeneration SELECT * FROM submission\")\n", "labels": {"reads": [{"table": "submission", "columns": null}], "writes": [{"table": "wastegeneration", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO defense_projects SELECT don_name, credit_score FROM healthydelights WHERE don_name > 429\"\n", "labels": {"reads": [{"table": "healthydelights", "columns": ["don_name", "credit_score"]}], "writes": [{"table": "defense_projects", "columns": ["don_name", "credit_score"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.purchase_date > 151).all()\n# src table: county_public_safety\nengine.execute(\"INSERT INTO mart_cart_item_di SELECT * FROM county_public_safety\")\n", "labels": {"reads": [{"table": "county_public_safety", "columns": null}], "writes": [{"table": "mart_cart_item_di", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO students_enrollment SELECT other_hotel_details, market_value_in_billion FROM transportation_trips WHERE other_hotel_details > 248\"\n", "labels": {"reads": [{"table": "transportation_trips", "columns": ["other_hotel_details", "market_value_in_billion"]}], "writes": [{"table": "students_enrollment", "columns": ["other_hotel_details", "market_value_in_billion"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO unions SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mart_device_log_hourly depends on dp_articles\ndbt build --models mart_device_log_hourly --vars '{\"src\":\"dp_articles\"}'\n", "labels": {"reads": [{"table": "dp_articles", "columns": null}], "writes": [{"table": "mart_device_log_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT representative_name, produceid FROM teaches LIMIT 476;\nINSERT INTO mentalhealthscores SELECT memberid, payment_method, framework FROM crops_year WHERE memberid > 21;\nSQL\n", "labels": {"reads": [{"table": "teaches", "columns": ["representative_name", "produceid"]}, {"table": "crops_year", "columns": ["memberid", "payment_method", "framework"]}], "writes": [{"table": "mentalhealthscores", "columns": ["memberid", "payment_method", "framework"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"accessible_tech_categories\")\nsrc.write.insertInto(\"carbon_emissions\", overwrite=True)\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": null}], "writes": [{"table": "carbon_emissions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO results SELECT excavationid, volunteer_hours, pollution_id, news_outlet FROM dwd.dwd_risk_score_delta WHERE excavationid > 417\"\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": ["excavationid", "volunteer_hours", "pollution_id", "news_outlet"]}], "writes": [{"table": "results", "columns": ["excavationid", "volunteer_hours", "pollution_id", "news_outlet"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO solar_energy SELECT a.program_date, b.offense FROM dw.dw_inventory_delta a JOIN co2_emissions b ON a.studentname = b.studentname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dw.dw_inventory_delta", "columns": null}, {"table": "co2_emissions", "columns": null}], "writes": [{"table": "solar_energy", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sale_quantity, date_id FROM conservation_projects LIMIT 128;\nINSERT INTO courtcases SELECT height, fate FROM diplomacy_events WHERE height > 343;\nSQL\n", "labels": {"reads": [{"table": "conservation_projects", "columns": ["sale_quantity", "date_id"]}, {"table": "diplomacy_events", "columns": ["height", "fate"]}], "writes": [{"table": "courtcases", "columns": ["height", "fate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO space_agencies_2 (wellname, char_cells) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "space_agencies_2", "columns": ["wellname", "char_cells"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO show SELECT a.attribute_id, b.order_item_id FROM mental_health_parity_violations a JOIN ca_menu_items b ON a.personnelbranch = b.personnelbranch\"\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}, {"table": "ca_menu_items", "columns": null}], "writes": [{"table": "show", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"communication_scores\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"whale_sightings\")\n", "labels": {"reads": [{"table": "communication_scores", "columns": null}], "writes": [{"table": "whale_sightings", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO contract_negotiations_un SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO community_education_programs SELECT customer_name, star_rating_code, opponent_id, stu_phone FROM call_volume WHERE customer_name > 44\"\n", "labels": {"reads": [{"table": "call_volume", "columns": ["customer_name", "star_rating_code", "opponent_id", "stu_phone"]}], "writes": [{"table": "community_education_programs", "columns": ["customer_name", "star_rating_code", "opponent_id", "stu_phone"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"fleet_management\")\nsink_to_sink(df, \"bi.bi_orders_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}], "writes": [{"table": "bi.bi_orders_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table recreation_centers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "recreation_centers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO service_budget (cases_count, customer_last_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "service_budget", "columns": ["cases_count", "customer_last_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"store\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"flu_cases\")\n", "labels": {"reads": [{"table": "store", "columns": null}], "writes": [{"table": "flu_cases", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.shop_id > 70).all()\n# src table: whale_sharks\nengine.execute(\"INSERT INTO home_game SELECT * FROM whale_sharks\")\n", "labels": {"reads": [{"table": "whale_sharks", "columns": null}], "writes": [{"table": "home_game", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO team SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gender SELECT is_electric, work_type FROM public.developers WHERE is_electric > 204\"\n", "labels": {"reads": [{"table": "public.developers", "columns": ["is_electric", "work_type"]}], "writes": [{"table": "gender", "columns": ["is_electric", "work_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model carbon_prices depends on artwork\ndbt build --models carbon_prices --vars '{\"source_table\":\"artwork\"}'\n", "labels": {"reads": [{"table": "artwork", "columns": null}], "writes": [{"table": "carbon_prices", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hours_developed, fda_approved FROM safety_testing LIMIT 247;\nINSERT INTO ods.ods_campaigns_delta SELECT artifacttype, therapy_date FROM stg.coupon_use_delta WHERE artifacttype > 471;\nSQL\n", "labels": {"reads": [{"table": "safety_testing", "columns": ["hours_developed", "fda_approved"]}, {"table": "stg.coupon_use_delta", "columns": ["artifacttype", "therapy_date"]}], "writes": [{"table": "ods.ods_campaigns_delta", "columns": ["artifacttype", "therapy_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"art_pieces\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tech_workers_union\")\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": [{"table": "tech_workers_union", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vulnerabilities --columns purchases,customer_type_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vulnerabilities", "columns": ["purchases", "customer_type_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.supplierid > 240).all()\n# src table: taxi_data\nengine.execute(\"INSERT INTO bus_fares SELECT * FROM taxi_data\")\n", "labels": {"reads": [{"table": "taxi_data", "columns": null}], "writes": [{"table": "bus_fares", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"wind_energy\")\nsrc.write.insertInto(\"riskassessments\", overwrite=True)\n", "labels": {"reads": [{"table": "wind_energy", "columns": null}], "writes": [{"table": "riskassessments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT billingid, fund_type FROM fossil_fuel_vehicles_japan LIMIT 33;\nINSERT INTO affiliated_with SELECT dormid, aircraft, clean_jerk FROM dallas_fire_incidents WHERE dormid > 295;\nSQL\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": ["billingid", "fund_type"]}, {"table": "dallas_fire_incidents", "columns": ["dormid", "aircraft", "clean_jerk"]}], "writes": [{"table": "affiliated_with", "columns": ["dormid", "aircraft", "clean_jerk"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table airport_aircraft --target-dir /tmp/land\n", "labels": {"reads": [{"table": "airport_aircraft", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"carbon_prices\")\nwrite_to_target(df, \"bioprocess.engineering_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "carbon_prices", "columns": null}], "writes": [{"table": "bioprocess.engineering_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT product_stock_number, genrename FROM agroecology_practices LIMIT 368\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO militaryequipment SELECT player_api_id, style FROM dws.dws_campaigns_df WHERE player_api_id > 474\")\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": ["product_stock_number", "genrename"]}, {"table": "dws.dws_campaigns_df", "columns": ["player_api_id", "style"]}], "writes": [{"table": "militaryequipment", "columns": ["player_api_id", "style"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sustainable_projects SELECT year_founded, silver, position, reports_to FROM legal_technology_funding WHERE year_founded > 409\"\n", "labels": {"reads": [{"table": "legal_technology_funding", "columns": ["year_founded", "silver", "position", "reports_to"]}], "writes": [{"table": "sustainable_projects", "columns": ["year_founded", "silver", "position", "reports_to"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO tourism_centers SELECT official_native_language, area, song_release_year FROM city_labor_cost WHERE official_native_language > 327\")\n", "labels": {"reads": [{"table": "city_labor_cost", "columns": ["official_native_language", "area", "song_release_year"]}], "writes": [{"table": "tourism_centers", "columns": ["official_native_language", "area", "song_release_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO studentaccommodations SELECT born_state, mine_type, galleryname, dishname FROM ods.member_point_df WHERE born_state > 97;\nEOF\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": ["born_state", "mine_type", "galleryname", "dishname"]}], "writes": [{"table": "studentaccommodations", "columns": ["born_state", "mine_type", "galleryname", "dishname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model clothing_brands depends on resource_extraction\ndbt run --models clothing_brands --vars 'source: resource_extraction'\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": [{"table": "clothing_brands", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.events_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO athlete_stats SELECT * FROM legacy\ncur.execute(\"SELECT manufacturer, trip_start_time FROM mart.inventory_hourly LIMIT 371\")\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": ["manufacturer", "trip_start_time"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table shark_biomass --columns tier,maintenance_contract_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "shark_biomass", "columns": ["tier", "maintenance_contract_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM review\", conn)\ndf.to_sql(\"ods.ods_campaigns_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "review", "columns": null}], "writes": [{"table": "ods.ods_campaigns_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO manufacturingplants SELECT unit_price, session_id, received_date, machine_series FROM products_booked WHERE unit_price > 400\")\n", "labels": {"reads": [{"table": "products_booked", "columns": ["unit_price", "session_id", "received_date", "machine_series"]}], "writes": [{"table": "manufacturingplants", "columns": ["unit_price", "session_id", "received_date", "machine_series"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO incident_region (mining_operation, total_distance) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "incident_region", "columns": ["mining_operation", "total_distance"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO record SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO org_climate_finance SELECT common_name, bus_number, school_id FROM movie WHERE common_name > 393\"\n", "labels": {"reads": [{"table": "movie", "columns": ["common_name", "bus_number", "school_id"]}], "writes": [{"table": "org_climate_finance", "columns": ["common_name", "bus_number", "school_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT visit_details, shipment_year FROM sustainable_building LIMIT 12\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO investmentsesg SELECT total_cost, centerid, min_depth, actor_id FROM bi_device_log_daily WHERE total_cost > 29\")\n", "labels": {"reads": [{"table": "sustainable_building", "columns": ["visit_details", "shipment_year"]}, {"table": "bi_device_log_daily", "columns": ["total_cost", "centerid", "min_depth", "actor_id"]}], "writes": [{"table": "investmentsesg", "columns": ["total_cost", "centerid", "min_depth", "actor_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT business_zone, show_times_per_day FROM ods.inventory_df\", engine)\nimport logging\ndf.to_sql(\"user_genre\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.inventory_df", "columns": ["business_zone", "show_times_per_day"]}], "writes": [{"table": "user_genre", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT technology, member FROM energy_production\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"assessment_notes\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "energy_production", "columns": ["technology", "member"]}], "writes": [{"table": "assessment_notes", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.crs_code > 210).all()\n# src table: bi_products\nengine.execute(\"INSERT INTO disaster_response SELECT * FROM bi_products\")\n", "labels": {"reads": [{"table": "bi_products", "columns": null}], "writes": [{"table": "disaster_response", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT waste_generation, num_songs FROM renewable_projects LIMIT 146;\nINSERT INTO excavation_sites SELECT policy_number, home_team_id, mission_name, archaeologist_id FROM threat_intelligence_budget WHERE policy_number > 69;\nSQL\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["waste_generation", "num_songs"]}, {"table": "threat_intelligence_budget", "columns": ["policy_number", "home_team_id", "mission_name", "archaeologist_id"]}], "writes": [{"table": "excavation_sites", "columns": ["policy_number", "home_team_id", "mission_name", "archaeologist_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dws.payments_delta SELECT memberid, financing_date, therapy_type FROM donationhistory WHERE memberid > 336;\nEOF\n", "labels": {"reads": [{"table": "donationhistory", "columns": ["memberid", "financing_date", "therapy_type"]}], "writes": [{"table": "dws.payments_delta", "columns": ["memberid", "financing_date", "therapy_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_treatment_facilities\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"models_safety\")\n", "labels": {"reads": [{"table": "water_treatment_facilities", "columns": null}], "writes": [{"table": "models_safety", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model device_log_df depends on players\ndbt run --select device_log_df --vars '{\"source_table\":\"players\"}'\n", "labels": {"reads": [{"table": "players", "columns": null}], "writes": [{"table": "device_log_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table customer_addresses --columns funding_source_type,shelter_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "customer_addresses", "columns": ["funding_source_type", "shelter_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO prices SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ingredients\"\n", "labels": {"reads": [{"table": "ingredients", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO baseball_teams (dept_name, chargeable_amount) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "baseball_teams", "columns": ["dept_name", "chargeable_amount"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO game_sales SELECT claimdate, organization, production_volume FROM drilling_rigs WHERE claimdate > 447\"\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": ["claimdate", "organization", "production_volume"]}], "writes": [{"table": "game_sales", "columns": ["claimdate", "organization", "production_volume"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT trial_name, billing_amount FROM rural_resources LIMIT 279\")\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ticketsales SELECT frequency, dock_count, permits_issued FROM eventparticipation WHERE frequency > 51\")\n", "labels": {"reads": [{"table": "rural_resources", "columns": ["trial_name", "billing_amount"]}, {"table": "eventparticipation", "columns": ["frequency", "dock_count", "permits_issued"]}], "writes": [{"table": "ticketsales", "columns": ["frequency", "dock_count", "permits_issued"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO forest_species SELECT meter_300, gold FROM dw_risk_score_daily WHERE meter_300 > 36\"\n", "labels": {"reads": [{"table": "dw_risk_score_daily", "columns": ["meter_300", "gold"]}], "writes": [{"table": "forest_species", "columns": ["meter_300", "gold"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO therapy SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO organic_farms SELECT sid, sport_id, booked_amount FROM leed_buildings WHERE sid > 250\"\n", "labels": {"reads": [{"table": "leed_buildings", "columns": ["sid", "sport_id", "booked_amount"]}], "writes": [{"table": "organic_farms", "columns": ["sid", "sport_id", "booked_amount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO new_schedules SELECT a.product_type_code, b.visit_month FROM genderdistribution a JOIN participation b ON a.galleryid = b.galleryid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "genderdistribution", "columns": null}, {"table": "participation", "columns": null}], "writes": [{"table": "new_schedules", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO medals SELECT production_quantity, sitename, patient_age FROM restorative_justice_programs WHERE production_quantity > 201\"\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": ["production_quantity", "sitename", "patient_age"]}], "writes": [{"table": "medals", "columns": ["production_quantity", "sitename", "patient_age"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tourist_attractions SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO review SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sales_by_quarter\", conn)\ndf.to_sql(\"stg.stg_coupon_use_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sales_by_quarter", "columns": null}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dissolved_oxygen, school_name FROM bi.bi_events_daily LIMIT 378\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["dissolved_oxygen", "school_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"jp_schema.policy_areas\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sfc_articles\")\n", "labels": {"reads": [{"table": "jp_schema.policy_areas", "columns": null}], "writes": [{"table": "sfc_articles", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_account_opened, mine_type FROM indigenouscommunities\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"ods_clicks_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "indigenouscommunities", "columns": ["date_account_opened", "mine_type"]}], "writes": [{"table": "ods_clicks_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_exposure_hourly\"\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vaccine_administered (ai_powered_features, inclusivehousing) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vaccine_administered", "columns": ["ai_powered_features", "inclusivehousing"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO industrial_building_energy_efficiency SELECT a.mascot, b.mediatorid FROM rural.bus_trips a JOIN stg.stg_users_di b ON a.contractorname = b.contractorname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rural.bus_trips", "columns": null}, {"table": "stg.stg_users_di", "columns": null}], "writes": [{"table": "industrial_building_energy_efficiency", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.inventory_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"park\")\nupsert_to_warehouse(df, \"gamesessions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "park", "columns": null}], "writes": [{"table": "gamesessions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT funder, stu_gpa FROM culturalcompetency\", engine)\nmetrics.append(round(score, 4))\nimport logging\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"gamedata\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "culturalcompetency", "columns": ["funder", "stu_gpa"]}], "writes": [{"table": "gamedata", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model continents depends on view_unit_status\ndbt run --models continents --vars 'source: view_unit_status'\n", "labels": {"reads": [{"table": "view_unit_status", "columns": null}], "writes": [{"table": "continents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT charging_level, assists FROM zipcodes LIMIT 356\")\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO chemical_production_5 SELECT flightid, extractiondate, updated_at FROM caribbeansea WHERE flightid > 474\")\n", "labels": {"reads": [{"table": "zipcodes", "columns": ["charging_level", "assists"]}, {"table": "caribbeansea", "columns": ["flightid", "extractiondate", "updated_at"]}], "writes": [{"table": "chemical_production_5", "columns": ["flightid", "extractiondate", "updated_at"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO street_markets SELECT * FROM legacy\ncur.execute(\"SELECT mine_name, financially_capable FROM dwd_risk_score_hourly LIMIT 378\")\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": ["mine_name", "financially_capable"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT well_type, author_community FROM ads.sessions_hourly LIMIT 10\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO gamedesign SELECT trend, protein_name, paritystatus, height FROM evsales WHERE trend > 164\")\n", "labels": {"reads": [{"table": "ads.sessions_hourly", "columns": ["well_type", "author_community"]}, {"table": "evsales", "columns": ["trend", "protein_name", "paritystatus", "height"]}], "writes": [{"table": "gamedesign", "columns": ["trend", "protein_name", "paritystatus", "height"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO marketingbudget (equipment_name, startup_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "marketingbudget", "columns": ["equipment_name", "startup_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stateinfrastructure (movieid, rating) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stateinfrastructure", "columns": ["movieid", "rating"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ocean_salinity SELECT a.borough, b.name_full FROM school_districts a JOIN state_budget b ON a.asset_details = b.asset_details\"\n", "labels": {"reads": [{"table": "school_districts", "columns": null}, {"table": "state_budget", "columns": null}], "writes": [{"table": "ocean_salinity", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO investment_rounds SELECT a.biome_id, b.test_result FROM artsheritage a JOIN stg.stg_inventory_hourly b ON a.dish = b.dish\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artsheritage", "columns": null}, {"table": "stg.stg_inventory_hourly", "columns": null}], "writes": [{"table": "investment_rounds", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.excavationid > 390).all()\n# src table: oceania_countries\nengine.execute(\"INSERT INTO game_sales SELECT * FROM oceania_countries\")\n", "labels": {"reads": [{"table": "oceania_countries", "columns": null}], "writes": [{"table": "game_sales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"participants\")\npush_to_target(df, \"ads_payments_daily\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "participants", "columns": null}], "writes": [{"table": "ads_payments_daily", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"vessel\")\nsrc.write.insertInto(\"news_views\", overwrite=True)\n", "labels": {"reads": [{"table": "vessel", "columns": null}], "writes": [{"table": "news_views", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO cybersecuritybudget SELECT sellingprice, stuid, frequency, make FROM claims_documents WHERE sellingprice > 73\"\n", "labels": {"reads": [{"table": "claims_documents", "columns": ["sellingprice", "stuid", "frequency", "make"]}], "writes": [{"table": "cybersecuritybudget", "columns": ["sellingprice", "stuid", "frequency", "make"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO factory_water SELECT a.is_valid, b.leader_name FROM renewable_projects a JOIN solar_plants b ON a.price_in_dollars = b.price_in_dollars\"\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}, {"table": "solar_plants", "columns": null}], "writes": [{"table": "factory_water", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bustrips\")\nsrc.write.insertInto(\"upgrades\", overwrite=True)\n", "labels": {"reads": [{"table": "bustrips", "columns": null}], "writes": [{"table": "upgrades", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.device_log_hourly SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT profession_count, shipment_tracking_number FROM canals LIMIT 303\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "canals", "columns": ["profession_count", "shipment_tracking_number"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dw_payments\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table dwd.dwd_events_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"degrees\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.mart_campaigns_daily\")\n", "labels": {"reads": [{"table": "degrees", "columns": null}], "writes": [{"table": "mart.mart_campaigns_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table property_community --columns party_id,creationyear --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "property_community", "columns": ["party_id", "creationyear"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table device --columns tree_species,investor_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "device", "columns": ["tree_species", "investor_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table colorado_river_basin --target-dir /tmp/land\n", "labels": {"reads": [{"table": "colorado_river_basin", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bridgerainfall\"\n", "labels": {"reads": [{"table": "bridgerainfall", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dispensarysales\", conn)\ndf.to_sql(\"investments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dispensarysales", "columns": null}], "writes": [{"table": "investments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO advocacy SELECT a.milestone, b.party_name FROM trainmaintenance a JOIN energy_efficiency_programs b ON a.characteristic_type_code = b.characteristic_type_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": null}, {"table": "energy_efficiency_programs", "columns": null}], "writes": [{"table": "advocacy", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table health_equity_metrics --columns supplierid,cmi_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "health_equity_metrics", "columns": ["supplierid", "cmi_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ai_safety SELECT enr, musical_id, soil_moisture FROM pitstops WHERE enr > 446\"\n", "labels": {"reads": [{"table": "pitstops", "columns": ["enr", "musical_id", "soil_moisture"]}], "writes": [{"table": "ai_safety", "columns": ["enr", "musical_id", "soil_moisture"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table rebounds --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rebounds", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"volunteer_registration\")\nsrc.write.insertInto(\"participants\", overwrite=True)\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": [{"table": "participants", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO offender_demographics SELECT brand_name, therapy_sessions FROM recycledmaterialsgarments WHERE brand_name > 278\")\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": ["brand_name", "therapy_sessions"]}], "writes": [{"table": "offender_demographics", "columns": ["brand_name", "therapy_sessions"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_shipments\").toPandas()\ndf[[\"awards\", \"contact_staff_id\"]].to_sql(\"dorm_amenity\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": ["awards", "contact_staff_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table county_public_safety --target-dir /tmp/land\n", "labels": {"reads": [{"table": "county_public_safety", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.refunds\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"innovation_grants\")\n", "labels": {"reads": [{"table": "ads.refunds", "columns": null}], "writes": [{"table": "innovation_grants", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_events_full SELECT player, round_type FROM crimes WHERE player > 34\"\n", "labels": {"reads": [{"table": "crimes", "columns": ["player", "round_type"]}], "writes": [{"table": "bi.bi_events_full", "columns": ["player", "round_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.role_name > 196).all()\n# src table: foodsafetyrecords\nengine.execute(\"INSERT INTO mart_exposure_hourly SELECT * FROM foodsafetyrecords\")\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": null}], "writes": [{"table": "mart_exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO continent SELECT 1\"\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO check_ins SELECT commission_pct, practices, date_incident_end FROM customer_master_index WHERE commission_pct > 498\"\n", "labels": {"reads": [{"table": "customer_master_index", "columns": ["commission_pct", "practices", "date_incident_end"]}], "writes": [{"table": "check_ins", "columns": ["commission_pct", "practices", "date_incident_end"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO bi_device_log_daily SELECT certification_id, steps, access_date, community_members FROM spacecraftspeed WHERE certification_id > 335;\nEOF\n", "labels": {"reads": [{"table": "spacecraftspeed", "columns": ["certification_id", "steps", "access_date", "community_members"]}], "writes": [{"table": "bi_device_log_daily", "columns": ["certification_id", "steps", "access_date", "community_members"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT deaths, product FROM drills LIMIT 118\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "drills", "columns": ["deaths", "product"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO electric_taxis SELECT a.artifact_type, b.trade FROM player_f a JOIN bank b ON a.investment_date = b.investment_date\"\n", "labels": {"reads": [{"table": "player_f", "columns": null}, {"table": "bank", "columns": null}], "writes": [{"table": "electric_taxis", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO salesperson SELECT playlist_id, grant_id, advisor FROM navalequipmentmaintenance WHERE playlist_id > 310\"], check=True)\n", "labels": {"reads": [{"table": "navalequipmentmaintenance", "columns": ["playlist_id", "grant_id", "advisor"]}], "writes": [{"table": "salesperson", "columns": ["playlist_id", "grant_id", "advisor"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"emergencyservices\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "emergencyservices", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"agri_innovations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dws.dws_member_point_di\")\n", "labels": {"reads": [{"table": "agri_innovations", "columns": null}], "writes": [{"table": "dws.dws_member_point_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table gamedesigndata --target-dir /tmp/land\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemicals\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"casesbyyear\")\n", "labels": {"reads": [{"table": "chemicals", "columns": null}], "writes": [{"table": "casesbyyear", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws_coupon_use SELECT don_id, handling_id, chemical_name, maintenance_type FROM movie WHERE don_id > 188\"], check=True)\n", "labels": {"reads": [{"table": "movie", "columns": ["don_id", "handling_id", "chemical_name", "maintenance_type"]}], "writes": [{"table": "dws_coupon_use", "columns": ["don_id", "handling_id", "chemical_name", "maintenance_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO hotel_reviews SELECT a.retailer, b.creation_date FROM city_waste_generation a JOIN satellites_in_orbit b ON a.health_equity_metric_2 = b.health_equity_metric_2\"\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": null}, {"table": "satellites_in_orbit", "columns": null}], "writes": [{"table": "hotel_reviews", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO consumer_preference SELECT goldid, storename FROM agriculturalinnovations WHERE goldid > 238\"\n", "labels": {"reads": [{"table": "agriculturalinnovations", "columns": ["goldid", "storename"]}], "writes": [{"table": "consumer_preference", "columns": ["goldid", "storename"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incident_type_code, date_claim_made FROM energy_consumption LIMIT 36\")\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO safe_dataset SELECT address_line_2, project_name FROM foodaid WHERE address_line_2 > 92\")\n", "labels": {"reads": [{"table": "energy_consumption", "columns": ["incident_type_code", "date_claim_made"]}, {"table": "foodaid", "columns": ["address_line_2", "project_name"]}], "writes": [{"table": "safe_dataset", "columns": ["address_line_2", "project_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO container SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO humanitarianmissions SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO cybersecuritybudget SELECT campaign_id, feedtype FROM wrestler WHERE campaign_id > 379\"\n", "labels": {"reads": [{"table": "wrestler", "columns": ["campaign_id", "feedtype"]}], "writes": [{"table": "cybersecuritybudget", "columns": ["campaign_id", "feedtype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO screen_mode SELECT a.units_owned, b.order_item_status FROM rigs a JOIN incidents_by_month b ON a.floor_exercise_points = b.floor_exercise_points\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rigs", "columns": null}, {"table": "incidents_by_month", "columns": null}], "writes": [{"table": "screen_mode", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO disabilitysupportprograms SELECT a.curator, b.trainingyear FROM vessels a JOIN education_union b ON a.points = b.points\"\n", "labels": {"reads": [{"table": "vessels", "columns": null}, {"table": "education_union", "columns": null}], "writes": [{"table": "disabilitysupportprograms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_products_full SELECT cname, license_number, speciesid FROM results WHERE cname > 383\"\n", "labels": {"reads": [{"table": "results", "columns": ["cname", "license_number", "speciesid"]}], "writes": [{"table": "ads.ads_products_full", "columns": ["cname", "license_number", "speciesid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO pitstops SELECT a.rank, b.factory FROM vehicle_sales a JOIN school_enrollment b ON a.extraction_amount = b.extraction_amount\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "vehicle_sales", "columns": null}, {"table": "school_enrollment", "columns": null}], "writes": [{"table": "pitstops", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table defense_diplomacy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO research SELECT arrival_time, ssn FROM mart_refunds WHERE arrival_time > 434\"], check=True)\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["arrival_time", "ssn"]}], "writes": [{"table": "research", "columns": ["arrival_time", "ssn"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"socially_responsible_loans\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"customer_master_index\")\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": null}], "writes": [{"table": "customer_master_index", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM hires\"\n", "labels": {"reads": [{"table": "hires", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO premises SELECT * FROM legacy\ncur.execute(\"SELECT fleet_id, complaintid FROM communitycourtcases LIMIT 160\")\n", "labels": {"reads": [{"table": "communitycourtcases", "columns": ["fleet_id", "complaintid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO tech_volunteers SELECT operationdate, booking_id FROM dw_users_full WHERE operationdate > 255\")\n", "labels": {"reads": [{"table": "dw_users_full", "columns": ["operationdate", "booking_id"]}], "writes": [{"table": "tech_volunteers", "columns": ["operationdate", "booking_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO screen_mode SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO wind_energy_projects SELECT ngo_id, org_size, used_kb, project FROM shrimp_farms WHERE ngo_id > 352\"\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["ngo_id", "org_size", "used_kb", "project"]}], "writes": [{"table": "wind_energy_projects", "columns": ["ngo_id", "org_size", "used_kb", "project"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"useracct\")\n", "labels": {"reads": [{"table": "ads", "columns": null}], "writes": [{"table": "useracct", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model fleet depends on ai_projects\ndbt run --models fleet --vars '{\"source_table\":\"ai_projects\"}'\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": [{"table": "fleet", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table songs_length --target-dir /tmp/land\n", "labels": {"reads": [{"table": "songs_length", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tv_shows_genre SELECT budget_in_billions, frequency FROM developers WHERE budget_in_billions > 4\"\n", "labels": {"reads": [{"table": "developers", "columns": ["budget_in_billions", "frequency"]}], "writes": [{"table": "tv_shows_genre", "columns": ["budget_in_billions", "frequency"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"farms\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "farms", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_coupon_use\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT passengers, treatment_id FROM forestry_practices LIMIT 166\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO jupiter_spacecraft SELECT produceid, login_name, completion_date FROM dwd.dwd_orders_daily WHERE produceid > 260\")\n", "labels": {"reads": [{"table": "forestry_practices", "columns": ["passengers", "treatment_id"]}, {"table": "dwd.dwd_orders_daily", "columns": ["produceid", "login_name", "completion_date"]}], "writes": [{"table": "jupiter_spacecraft", "columns": ["produceid", "login_name", "completion_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.risk_score_di\")\nsrc.write.insertInto(\"project_duration\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}], "writes": [{"table": "project_duration", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO disabilityadvocacy (hotel_name, name_full) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "disabilityadvocacy", "columns": ["hotel_name", "name_full"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT floor_exercise_points, watch_time FROM libraries\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"indie_artists\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "libraries", "columns": ["floor_exercise_points", "watch_time"]}], "writes": [{"table": "indie_artists", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.implementation_year > 360).all()\n# src table: urban_initiatives\nengine.execute(\"INSERT INTO game_scores SELECT * FROM urban_initiatives\")\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "game_scores", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dwd.dwd_campaigns_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table medicine --columns content_id,commodity --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "medicine", "columns": ["content_id", "commodity"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"stg.stg_events_hourly\")\nsave_to_output(df, \"articles_es\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg.stg_events_hourly", "columns": null}], "writes": [{"table": "articles_es", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO public_transportation_routes SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO status SELECT a.attraction_type_description, b.faculty FROM communityengagement a JOIN solana_transactions b ON a.other_item_details = b.other_item_details\"\n", "labels": {"reads": [{"table": "communityengagement", "columns": null}, {"table": "solana_transactions", "columns": null}], "writes": [{"table": "status", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"carbon_prices\")\nsink_to_store(df, \"perpetrator\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "carbon_prices", "columns": null}], "writes": [{"table": "perpetrator", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sessiondate, ironid FROM ads_exposure_hourly LIMIT 133\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["sessiondate", "ironid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM program_budget\", conn)\ndf.to_sql(\"healthequitymetrics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "program_budget", "columns": null}], "writes": [{"table": "healthequitymetrics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM arrivals\", conn)\ndf.to_sql(\"org_volunteer\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "arrivals", "columns": null}], "writes": [{"table": "org_volunteer", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO explainable_ai SELECT wins, satellite FROM prison WHERE wins > 240\"\n", "labels": {"reads": [{"table": "prison", "columns": ["wins", "satellite"]}], "writes": [{"table": "explainable_ai", "columns": ["wins", "satellite"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bridges (denomination, community_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bridges", "columns": ["denomination", "community_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO eu_humanitarian_assistance SELECT safety_record, away_team_three_point FROM safety_incidents WHERE safety_record > 76\"\n", "labels": {"reads": [{"table": "safety_incidents", "columns": ["safety_record", "away_team_three_point"]}], "writes": [{"table": "eu_humanitarian_assistance", "columns": ["safety_record", "away_team_three_point"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT acc_regular_season, port_code FROM community_events LIMIT 301;\nINSERT INTO ads.orders_daily SELECT end_station_id, classroom, exit_strategy FROM student WHERE end_station_id > 327;\nSQL\n", "labels": {"reads": [{"table": "community_events", "columns": ["acc_regular_season", "port_code"]}, {"table": "student", "columns": ["end_station_id", "classroom", "exit_strategy"]}], "writes": [{"table": "ads.orders_daily", "columns": ["end_station_id", "classroom", "exit_strategy"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attraction_name, registered_date FROM bustrips\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"casesbyyear\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bustrips", "columns": ["attraction_name", "registered_date"]}], "writes": [{"table": "casesbyyear", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT community, tree_id FROM donation LIMIT 223\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "donation", "columns": ["community", "tree_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT transaction_type_description, royal_family_details FROM soil_moisture\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\ndf.to_sql(\"movie_ratings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["transaction_type_description", "royal_family_details"]}], "writes": [{"table": "movie_ratings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO soccer_teams SELECT category_name, policyholderid, museumid, volunteerdate FROM parties WHERE category_name > 353\"\n", "labels": {"reads": [{"table": "parties", "columns": ["category_name", "policyholderid", "museumid", "volunteerdate"]}], "writes": [{"table": "soccer_teams", "columns": ["category_name", "policyholderid", "museumid", "volunteerdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO cultural_competency_training SELECT * FROM legacy\ncur.execute(\"SELECT crop_name, accreditation_level FROM workout_sessions LIMIT 469\")\n", "labels": {"reads": [{"table": "workout_sessions", "columns": ["crop_name", "accreditation_level"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT date_assigned_to, archaeologist_id FROM un_peacekeeping_operations LIMIT 436\")\nimport logging\nspark.sql(\"INSERT INTO athletes SELECT assists, tourist_attraction_id FROM electricvehicleadoption WHERE assists > 477\")\n", "labels": {"reads": [{"table": "un_peacekeeping_operations", "columns": ["date_assigned_to", "archaeologist_id"]}, {"table": "electricvehicleadoption", "columns": ["assists", "tourist_attraction_id"]}], "writes": [{"table": "athletes", "columns": ["assists", "tourist_attraction_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO equipment SELECT era, plantid FROM coal_reserves WHERE era > 391\")\n", "labels": {"reads": [{"table": "coal_reserves", "columns": ["era", "plantid"]}], "writes": [{"table": "equipment", "columns": ["era", "plantid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO iron SELECT * FROM legacy\ncur.execute(\"SELECT reported_date, mhw_id FROM manufacturingplants LIMIT 356\")\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": ["reported_date", "mhw_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO veteran_employment SELECT a.lifespan, b.sale_date FROM countryintelligenceops a JOIN sales_quarterly b ON a.property_id = b.property_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "countryintelligenceops", "columns": null}, {"table": "sales_quarterly", "columns": null}], "writes": [{"table": "veteran_employment", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cosmetics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"healthcare_budget\")\n", "labels": {"reads": [{"table": "cosmetics", "columns": null}], "writes": [{"table": "healthcare_budget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT passenger_count, date_id FROM waste\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"scientists\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "waste", "columns": ["passenger_count", "date_id"]}], "writes": [{"table": "scientists", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT representative_name, vegetable FROM eu_humanitarian_assistance LIMIT 92\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO stg.refunds SELECT vessel_name, characteristic_id, school FROM ocean_acidification_antarctic WHERE vessel_name > 17\")\n", "labels": {"reads": [{"table": "eu_humanitarian_assistance", "columns": ["representative_name", "vegetable"]}, {"table": "ocean_acidification_antarctic", "columns": ["vessel_name", "characteristic_id", "school"]}], "writes": [{"table": "stg.refunds", "columns": ["vessel_name", "characteristic_id", "school"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM complaints\", conn)\ndf.to_sql(\"wastedata\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "complaints", "columns": null}], "writes": [{"table": "wastedata", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO minor_in SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.member_point_full\", conn)\ndf.to_sql(\"underwater_cables\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": null}], "writes": [{"table": "underwater_cables", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO teams SELECT countid, water_depth, activity_name, account_name FROM stg.sessions_full WHERE countid > 287\"\n", "labels": {"reads": [{"table": "stg.sessions_full", "columns": ["countid", "water_depth", "activity_name", "account_name"]}], "writes": [{"table": "teams", "columns": ["countid", "water_depth", "activity_name", "account_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.payments_di SELECT a.writer, b.destruction_authorised_by_employee_id FROM province.human_rights_data a JOIN ngo_funding b ON a.semester = b.semester\"\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": null}, {"table": "ngo_funding", "columns": null}], "writes": [{"table": "ads.payments_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM financial_capability\", conn)\ndf.to_sql(\"nba\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "financial_capability", "columns": null}], "writes": [{"table": "nba", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"education_programs\").toPandas()\ndf[[\"consumption\", \"state_id\"]].to_sql(\"employee\", engine, index=False)\n", "labels": {"reads": [{"table": "education_programs", "columns": null}], "writes": [{"table": "employee", "columns": ["consumption", "state_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO climate_mitigation_projects SELECT inspection_id, medication FROM mart_cart_item_di WHERE inspection_id > 23\")\n", "labels": {"reads": [{"table": "mart_cart_item_di", "columns": ["inspection_id", "medication"]}], "writes": [{"table": "climate_mitigation_projects", "columns": ["inspection_id", "medication"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"coffee_prices\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"circular_supply_chain_products\")\n", "labels": {"reads": [{"table": "coffee_prices", "columns": null}], "writes": [{"table": "circular_supply_chain_products", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"family_cases\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"geneva_motor_show\")\n", "labels": {"reads": [{"table": "family_cases", "columns": null}], "writes": [{"table": "geneva_motor_show", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads SELECT record_id, gender FROM project_timeline WHERE record_id > 239;\nEOF\n", "labels": {"reads": [{"table": "project_timeline", "columns": ["record_id", "gender"]}], "writes": [{"table": "ads", "columns": ["record_id", "gender"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM product\", conn)\ndf.to_sql(\"educationprograms\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "product", "columns": null}], "writes": [{"table": "educationprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recreation_centers SELECT * FROM legacy\ncur.execute(\"SELECT meal_id, clean_jerk FROM trip LIMIT 202\")\n", "labels": {"reads": [{"table": "trip", "columns": ["meal_id", "clean_jerk"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO digital_divide_initiatives SELECT snatch, fleet_id, num_sustainable_materials, number_cities FROM marketing_regions WHERE snatch > 250\"\n", "labels": {"reads": [{"table": "marketing_regions", "columns": ["snatch", "fleet_id", "num_sustainable_materials", "number_cities"]}], "writes": [{"table": "digital_divide_initiatives", "columns": ["snatch", "fleet_id", "num_sustainable_materials", "number_cities"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO professionals SELECT name_first, kills, build_date FROM bike_stations WHERE name_first > 319;\nEOF\n", "labels": {"reads": [{"table": "bike_stations", "columns": ["name_first", "kills", "build_date"]}], "writes": [{"table": "professionals", "columns": ["name_first", "kills", "build_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table shariah_compliant_finance --columns accessible,base_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "shariah_compliant_finance", "columns": ["accessible", "base_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO playergamedata SELECT number_of_vessels, spacecraft_id, property_id FROM winter_olympics WHERE number_of_vessels > 91\"\n", "labels": {"reads": [{"table": "winter_olympics", "columns": ["number_of_vessels", "spacecraft_id", "property_id"]}], "writes": [{"table": "playergamedata", "columns": ["number_of_vessels", "spacecraft_id", "property_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table labor_hours --target-dir /tmp/land\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd_sessions_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT exhibitionid, platform_id FROM student_tests_taken\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"game_results\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": ["exhibitionid", "platform_id"]}], "writes": [{"table": "game_results", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.maintenance_type > 330).all()\n# src table: characteristics\nengine.execute(\"INSERT INTO region_stats SELECT * FROM characteristics\")\n", "labels": {"reads": [{"table": "characteristics", "columns": null}], "writes": [{"table": "region_stats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO iron SELECT a.product_name, b.architect_id FROM energy_efficiency_programs a JOIN storage_projects b ON a.usage = b.usage\"\n", "labels": {"reads": [{"table": "energy_efficiency_programs", "columns": null}, {"table": "storage_projects", "columns": null}], "writes": [{"table": "iron", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vehiclemodels\", conn)\ndf.to_sql(\"fair_trade_suppliers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": null}], "writes": [{"table": "fair_trade_suppliers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cyber_incidents SELECT a.other_item_details, b.emp_lname FROM team_members a JOIN streams b ON a.date_in_locaton_to = b.date_in_locaton_to\"\n", "labels": {"reads": [{"table": "team_members", "columns": null}, {"table": "streams", "columns": null}], "writes": [{"table": "cyber_incidents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO artist SELECT users_engaged, gamegenre, date_incident_end FROM fish_biomass WHERE users_engaged > 346\"], check=True)\n", "labels": {"reads": [{"table": "fish_biomass", "columns": ["users_engaged", "gamegenre", "date_incident_end"]}], "writes": [{"table": "artist", "columns": ["users_engaged", "gamegenre", "date_incident_end"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"invoice_lines\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"menu_engineering\")\n", "labels": {"reads": [{"table": "invoice_lines", "columns": null}], "writes": [{"table": "menu_engineering", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO status SELECT a.currency, b.feature_id FROM policy_feedback a JOIN sustainable_projects b ON a.num_songs = b.num_songs\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "policy_feedback", "columns": null}, {"table": "sustainable_projects", "columns": null}], "writes": [{"table": "status", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO foodsafetyrecords SELECT avg_depth, emissions FROM vessel_safety WHERE avg_depth > 129\"\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["avg_depth", "emissions"]}], "writes": [{"table": "foodsafetyrecords", "columns": ["avg_depth", "emissions"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"animal_population_status\")\nsrc.write.insertInto(\"has_amenity\", overwrite=True)\n", "labels": {"reads": [{"table": "animal_population_status", "columns": null}], "writes": [{"table": "has_amenity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM exhibitiondetails\"\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM drought_impact\"\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"faculty_participates_in\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table appellations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "appellations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO match SELECT ticketprice, uk_vat_number, hoursperweek FROM lessons WHERE ticketprice > 148\"\n", "labels": {"reads": [{"table": "lessons", "columns": ["ticketprice", "uk_vat_number", "hoursperweek"]}], "writes": [{"table": "match", "columns": ["ticketprice", "uk_vat_number", "hoursperweek"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM satelliteimagery\"\n", "labels": {"reads": [{"table": "satelliteimagery", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ocean_floor SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"safetytestingcounts\")\npersist_to_sink(df, \"threat_intel\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "safetytestingcounts", "columns": null}], "writes": [{"table": "threat_intel", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO acidification_data (network, severity) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "acidification_data", "columns": ["network", "severity"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT assessmentdate, actual_order_id FROM bi_refunds_daily LIMIT 492\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO sustainability_initiatives SELECT statename, nominee, service_name, coupon_id FROM dorm_amenity WHERE statename > 264\")\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["assessmentdate", "actual_order_id"]}, {"table": "dorm_amenity", "columns": ["statename", "nominee", "service_name", "coupon_id"]}], "writes": [{"table": "sustainability_initiatives", "columns": ["statename", "nominee", "service_name", "coupon_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_campaigns SELECT * FROM legacy\ncur.execute(\"SELECT subscription_start_date, contractor_id FROM flight_safety LIMIT 471\")\n", "labels": {"reads": [{"table": "flight_safety", "columns": ["subscription_start_date", "contractor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cmi_cross_ref_id, inspectionid FROM warehouses\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"shared_scooters\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "warehouses", "columns": ["cmi_cross_ref_id", "inspectionid"]}], "writes": [{"table": "shared_scooters", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"digital_trends\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"menu_items\")\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}], "writes": [{"table": "menu_items", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM military_personnel\"\n", "labels": {"reads": [{"table": "military_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO ods.ods_exposure_delta SELECT a.join_year, b.trackid FROM mentalhealthproviders a JOIN nailpolishsales b ON a.pass_fail = b.pass_fail\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": null}, {"table": "nailpolishsales", "columns": null}], "writes": [{"table": "ods.ods_exposure_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table portfolios --target-dir /tmp/land\n", "labels": {"reads": [{"table": "portfolios", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT char_cells, element FROM ref_document_status LIMIT 451\")\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO street_markets SELECT gender_mf, location, group_equity_shareholding FROM vr_tech WHERE gender_mf > 424\")\n", "labels": {"reads": [{"table": "ref_document_status", "columns": ["char_cells", "element"]}, {"table": "vr_tech", "columns": ["gender_mf", "location", "group_equity_shareholding"]}], "writes": [{"table": "street_markets", "columns": ["gender_mf", "location", "group_equity_shareholding"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO movie_financials SELECT a.bioreactor_id, b.sustainable_practice FROM ethics_violations a JOIN mart.mart_refunds_di b ON a.visit_month = b.visit_month\"\n", "labels": {"reads": [{"table": "ethics_violations", "columns": null}, {"table": "mart.mart_refunds_di", "columns": null}], "writes": [{"table": "movie_financials", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO fair_trade_suppliers (stockid, artworkname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "fair_trade_suppliers", "columns": ["stockid", "artworkname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"incarcerated\")\nupsert_to_target(df, \"mining_operation_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "incarcerated", "columns": null}], "writes": [{"table": "mining_operation_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamedesigndata\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.risk_score_df (railway_id, airline) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.risk_score_df", "columns": ["railway_id", "airline"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.bname > 280).all()\n# src table: labor_unions\nengine.execute(\"INSERT INTO railway SELECT * FROM labor_unions\")\n", "labels": {"reads": [{"table": "labor_unions", "columns": null}], "writes": [{"table": "railway", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cultivators\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"province.human_rights_data\")\n", "labels": {"reads": [{"table": "cultivators", "columns": null}], "writes": [{"table": "province.human_rights_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO restorative_justice_programs SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"production_rare_earth_elements\")\ndump_to_target(df, \"office_locations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": null}], "writes": [{"table": "office_locations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO trainmaintenance SELECT shop_name, asset_disposed_date, grantid FROM india_ingredient_sourcing WHERE shop_name > 167\"], check=True)\n", "labels": {"reads": [{"table": "india_ingredient_sourcing", "columns": ["shop_name", "asset_disposed_date", "grantid"]}], "writes": [{"table": "trainmaintenance", "columns": ["shop_name", "asset_disposed_date", "grantid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO auto_show (acidification_level, conservation_status) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "auto_show", "columns": ["acidification_level", "conservation_status"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods.ods_users_daily\")\nsrc.write.insertInto(\"site\", overwrite=True)\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": [{"table": "site", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO justice_schemas.legal_tech_providers SELECT mine_id, employees, launched_year FROM mart.mart_vendors WHERE mine_id > 280\"\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": ["mine_id", "employees", "launched_year"]}], "writes": [{"table": "justice_schemas.legal_tech_providers", "columns": ["mine_id", "employees", "launched_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"rooms\")\nsave_to_sink(df, \"leo_missions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "rooms", "columns": null}], "writes": [{"table": "leo_missions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO hospitallocations SELECT form_id, numhearings FROM pilot_record WHERE form_id > 166\"\n", "labels": {"reads": [{"table": "pilot_record", "columns": ["form_id", "numhearings"]}], "writes": [{"table": "hospitallocations", "columns": ["form_id", "numhearings"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_contact_to, shares FROM ads_coupon_use_full\", engine)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"festivals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads_coupon_use_full", "columns": ["date_contact_to", "shares"]}], "writes": [{"table": "festivals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT enr, ingredient FROM ods.ods_risk_score_full\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"player_f\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_risk_score_full", "columns": ["enr", "ingredient"]}], "writes": [{"table": "player_f", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mediators\"\n", "labels": {"reads": [{"table": "mediators", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.clicks_di\")\nsrc.write.insertInto(\"communityhealthworkers\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.clicks_di", "columns": null}], "writes": [{"table": "communityhealthworkers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainable_practices_2 SELECT case_id, hospitalname, semester FROM climate_finance WHERE case_id > 43\"\n", "labels": {"reads": [{"table": "climate_finance", "columns": ["case_id", "hospitalname", "semester"]}], "writes": [{"table": "sustainable_practices_2", "columns": ["case_id", "hospitalname", "semester"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sentence_id > 370).all()\n# src table: investor_activities\nengine.execute(\"INSERT INTO service_budget SELECT * FROM investor_activities\")\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": [{"table": "service_budget", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM gradeconversion\", conn)\ndf.to_sql(\"ads_refunds_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "gradeconversion", "columns": null}], "writes": [{"table": "ads_refunds_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM domesticconferences\"\n", "labels": {"reads": [{"table": "domesticconferences", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"languagesatrisk\").toPandas()\ndf[[\"carbon_offset_tons\", \"review_date\"]].to_sql(\"tracklists\", engine, index=False)\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": null}], "writes": [{"table": "tracklists", "columns": ["carbon_offset_tons", "review_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO public_participation SELECT oppose_rate, fundingagency FROM shipment_data WHERE oppose_rate > 208\"\n", "labels": {"reads": [{"table": "shipment_data", "columns": ["oppose_rate", "fundingagency"]}], "writes": [{"table": "public_participation", "columns": ["oppose_rate", "fundingagency"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.ods_coupon_use_delta SELECT releaseyear, claimtype FROM autonomousvehicleaccidents WHERE releaseyear > 288;\nEOF\n", "labels": {"reads": [{"table": "autonomousvehicleaccidents", "columns": ["releaseyear", "claimtype"]}], "writes": [{"table": "ods.ods_coupon_use_delta", "columns": ["releaseyear", "claimtype"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sports\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"transportation_union\")\n", "labels": {"reads": [{"table": "sports", "columns": null}], "writes": [{"table": "transportation_union", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM prereq\", conn)\ndf.to_sql(\"ethical_ai\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "prereq", "columns": null}], "writes": [{"table": "ethical_ai", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_orders_hourly\"\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO review SELECT playerid, mission_name FROM dw.dw_users_di WHERE playerid > 134\"], check=True)\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": ["playerid", "mission_name"]}], "writes": [{"table": "review", "columns": ["playerid", "mission_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle_safety_testing\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"happy_hour\")\n", "labels": {"reads": [{"table": "vehicle_safety_testing", "columns": null}], "writes": [{"table": "happy_hour", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT region_id, completed FROM bi.bi_exposure_hourly\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"decentralized_applications\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": ["region_id", "completed"]}], "writes": [{"table": "decentralized_applications", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO farmer_details SELECT strainid, incident_type_code, sales_amount, rural_area FROM company WHERE strainid > 245\"\n", "labels": {"reads": [{"table": "company", "columns": ["strainid", "incident_type_code", "sales_amount", "rural_area"]}], "writes": [{"table": "farmer_details", "columns": ["strainid", "incident_type_code", "sales_amount", "rural_area"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO cargo_data SELECT a.rating_in_percent, b.unit_of_measure FROM levees a JOIN reporters b ON a.percentage_change = b.percentage_change\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "levees", "columns": null}, {"table": "reporters", "columns": null}], "writes": [{"table": "cargo_data", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_warehouses\").toPandas()\ndf[[\"salesperson\", \"contract_start\"]].to_sql(\"indian_ocean_wells\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": null}], "writes": [{"table": "indian_ocean_wells", "columns": ["salesperson", "contract_start"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table match_result --target-dir /tmp/land\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO mentalhealthproviders SELECT speciesid, rig_id, physical FROM miningoperations WHERE speciesid > 128\"\n", "labels": {"reads": [{"table": "miningoperations", "columns": ["speciesid", "rig_id", "physical"]}], "writes": [{"table": "mentalhealthproviders", "columns": ["speciesid", "rig_id", "physical"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO microfinance_clients SELECT restypedescription, reported_date, county, pressure FROM dysprosium_mines WHERE restypedescription > 373\"\n", "labels": {"reads": [{"table": "dysprosium_mines", "columns": ["restypedescription", "reported_date", "county", "pressure"]}], "writes": [{"table": "microfinance_clients", "columns": ["restypedescription", "reported_date", "county", "pressure"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fish_feed_factories\").toPandas()\ndf[[\"assists\", \"start_station_name\"]].to_sql(\"midwest_materials\", engine, index=False)\n", "labels": {"reads": [{"table": "fish_feed_factories", "columns": null}], "writes": [{"table": "midwest_materials", "columns": ["assists", "start_station_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ancient_artifacts\"\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM organic_farms\", conn)\ndf.to_sql(\"initiative_types\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "organic_farms", "columns": null}], "writes": [{"table": "initiative_types", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table military_personnel --columns environmental_impact_score,opening_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "military_personnel", "columns": ["environmental_impact_score", "opening_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incidentdate, num_volunteers FROM fertilizer LIMIT 382\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO hotel_chains SELECT coal_reserve_remaining, period, wage, inventoryid FROM concert_revenue WHERE coal_reserve_remaining > 324\")\n", "labels": {"reads": [{"table": "fertilizer", "columns": ["incidentdate", "num_volunteers"]}, {"table": "concert_revenue", "columns": ["coal_reserve_remaining", "period", "wage", "inventoryid"]}], "writes": [{"table": "hotel_chains", "columns": ["coal_reserve_remaining", "period", "wage", "inventoryid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_vendors_hourly SELECT safetytestdate, leadershiptraining FROM parties WHERE safetytestdate > 425\"\n", "labels": {"reads": [{"table": "parties", "columns": ["safetytestdate", "leadershiptraining"]}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": ["safetytestdate", "leadershiptraining"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table atlantic_ocean_fish --columns issue_month,stock --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "atlantic_ocean_fish", "columns": ["issue_month", "stock"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO incident_region (doctor_id, ticket_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "incident_region", "columns": ["doctor_id", "ticket_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT attack_count, class FROM bi.events_df LIMIT 124\")\nimport logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO debris SELECT participation_id, communityname, organisation_id, bioprocess_id FROM member_activity WHERE participation_id > 403\")\n", "labels": {"reads": [{"table": "bi.events_df", "columns": ["attack_count", "class"]}, {"table": "member_activity", "columns": ["participation_id", "communityname", "organisation_id", "bioprocess_id"]}], "writes": [{"table": "debris", "columns": ["participation_id", "communityname", "organisation_id", "bioprocess_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM eventdates\", conn)\ndf.to_sql(\"stg.refunds_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "eventdates", "columns": null}], "writes": [{"table": "stg.refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.school_code > 168).all()\n# src table: economic_diversification_argentina\nengine.execute(\"INSERT INTO materials SELECT * FROM economic_diversification_argentina\")\n", "labels": {"reads": [{"table": "economic_diversification_argentina", "columns": null}], "writes": [{"table": "materials", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"performingartsprograms\").toPandas()\ndf[[\"training_name\", \"oil_volume\"]].to_sql(\"researchpapers\", engine, index=False)\n", "labels": {"reads": [{"table": "performingartsprograms", "columns": null}], "writes": [{"table": "researchpapers", "columns": ["training_name", "oil_volume"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_orders\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"iron_ore_production\")\n", "labels": {"reads": [{"table": "ads.ads_orders", "columns": null}], "writes": [{"table": "iron_ore_production", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"upgrades\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "upgrades", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO food_justice_contributors SELECT team_id_loser, staff_first_name, match_id, veteran_id FROM workforce_training WHERE team_id_loser > 141\"\n", "labels": {"reads": [{"table": "workforce_training", "columns": ["team_id_loser", "staff_first_name", "match_id", "veteran_id"]}], "writes": [{"table": "food_justice_contributors", "columns": ["team_id_loser", "staff_first_name", "match_id", "veteran_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.stg_shipments_hourly --columns tourist_id,health_equity_metric_3 --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_shipments_hourly", "columns": ["tourist_id", "health_equity_metric_3"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"financial_capability_program\")\npush_to_store(df, \"transportation_union\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": null}], "writes": [{"table": "transportation_union", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO parts (ssn, matchdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "parts", "columns": ["ssn", "matchdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO swimmer SELECT train_number, publication_id FROM donors_region WHERE train_number > 353\"\n", "labels": {"reads": [{"table": "donors_region", "columns": ["train_number", "publication_id"]}], "writes": [{"table": "swimmer", "columns": ["train_number", "publication_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO driver SELECT stu_gpa, lastname, warehousename FROM cybersecurity_incidents WHERE stu_gpa > 373\"], check=True)\n", "labels": {"reads": [{"table": "cybersecurity_incidents", "columns": ["stu_gpa", "lastname", "warehousename"]}], "writes": [{"table": "driver", "columns": ["stu_gpa", "lastname", "warehousename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT pilot_id, classid FROM traffic_accidents LIMIT 416\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO mart.mart_payments_hourly SELECT amount_due, volume FROM product_characteristics WHERE amount_due > 21\")\n", "labels": {"reads": [{"table": "traffic_accidents", "columns": ["pilot_id", "classid"]}, {"table": "product_characteristics", "columns": ["amount_due", "volume"]}], "writes": [{"table": "mart.mart_payments_hourly", "columns": ["amount_due", "volume"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"explainableai\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "explainableai", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO tb_reports SELECT response_time, clientid FROM eco_hotels WHERE response_time > 446\"\n", "labels": {"reads": [{"table": "eco_hotels", "columns": ["response_time", "clientid"]}], "writes": [{"table": "tb_reports", "columns": ["response_time", "clientid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.task > 190).all()\n# src table: construction_labor_stats\nengine.execute(\"INSERT INTO seafoodsouthafricakenya SELECT * FROM construction_labor_stats\")\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_inventory_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"economic_diversification\")\n", "labels": {"reads": [{"table": "dws.dws_inventory_hourly", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO dws.dws_campaigns_df SELECT a.shale_play, b.prof_office FROM spending a JOIN carbon_prices b ON a.zone_id = b.zone_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "spending", "columns": null}, {"table": "carbon_prices", "columns": null}], "writes": [{"table": "dws.dws_campaigns_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO exhibition_visits SELECT established_date, leader_name, support_id FROM street_markets WHERE established_date > 259\"\n", "labels": {"reads": [{"table": "street_markets", "columns": ["established_date", "leader_name", "support_id"]}], "writes": [{"table": "exhibition_visits", "columns": ["established_date", "leader_name", "support_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stops SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.destination > 355).all()\n# src table: plays_games\nengine.execute(\"INSERT INTO visitor_exhibition SELECT * FROM plays_games\")\n", "labels": {"reads": [{"table": "plays_games", "columns": null}], "writes": [{"table": "visitor_exhibition", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO disabilityadvocacy SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO news_stories SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT aid_id, ticket_price FROM employment LIMIT 36\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "employment", "columns": ["aid_id", "ticket_price"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dwd.dwd_exposure_full SELECT art, airport FROM safetytesting WHERE art > 305;\nEOF\n", "labels": {"reads": [{"table": "safetytesting", "columns": ["art", "airport"]}], "writes": [{"table": "dwd.dwd_exposure_full", "columns": ["art", "airport"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO forms SELECT a.diversity_score, b.count_date FROM exhibition_record a JOIN dw.inventory_delta b ON a.startup_id = b.startup_id\"\n", "labels": {"reads": [{"table": "exhibition_record", "columns": null}, {"table": "dw.inventory_delta", "columns": null}], "writes": [{"table": "forms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table student_tests_taken --columns gender_code,acc_percent --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "student_tests_taken", "columns": ["gender_code", "acc_percent"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT loan_id, country FROM emergency_categories LIMIT 471\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "emergency_categories", "columns": ["loan_id", "country"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ods.ods_campaigns_df\", conn)\ndf.to_sql(\"habitat3\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_df", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ota_name, donor_program FROM monitoring_zones LIMIT 271\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "monitoring_zones", "columns": ["ota_name", "donor_program"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO songs (unit_price, artpiecename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "songs", "columns": ["unit_price", "artpiecename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO patient_outcomes SELECT hotel_chain_name, individual_middle_name, workoutdate, animal_species FROM heart_rate_data WHERE hotel_chain_name > 449\")\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": ["hotel_chain_name", "individual_middle_name", "workoutdate", "animal_species"]}], "writes": [{"table": "patient_outcomes", "columns": ["hotel_chain_name", "individual_middle_name", "workoutdate", "animal_species"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO biodiversity SELECT sustainability_initiative_id, away_team_id, onscholarship, num_fans FROM tech_accessibility_funding WHERE sustainability_initiative_id > 99;\nEOF\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": ["sustainability_initiative_id", "away_team_id", "onscholarship", "num_fans"]}], "writes": [{"table": "biodiversity", "columns": ["sustainability_initiative_id", "away_team_id", "onscholarship", "num_fans"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO spaceradar (spacecraft_name, concertid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "spaceradar", "columns": ["spacecraft_name", "concertid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"products_in_events\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ref_service_types\")\n", "labels": {"reads": [{"table": "products_in_events", "columns": null}], "writes": [{"table": "ref_service_types", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"materials\")\nsave_to_output(df, \"mart.mart_coupon_use_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "materials", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO patient SELECT trip_duration, menuitemid, investors, rebounds FROM manufacturer WHERE trip_duration > 422\"\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["trip_duration", "menuitemid", "investors", "rebounds"]}], "writes": [{"table": "patient", "columns": ["trip_duration", "menuitemid", "investors", "rebounds"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model body_builder depends on hotel_revenue\ndbt run --select body_builder --vars 'source: hotel_revenue'\n", "labels": {"reads": [{"table": "hotel_revenue", "columns": null}], "writes": [{"table": "body_builder", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO agricultural_projects SELECT funding_received, crime_rate, treasurer_vote, time_hour FROM caribbeansea WHERE funding_received > 217\")\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["funding_received", "crime_rate", "treasurer_vote", "time_hour"]}], "writes": [{"table": "agricultural_projects", "columns": ["funding_received", "crime_rate", "treasurer_vote", "time_hour"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cerium_production SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO civilcases SELECT employment_id, collection_id, publication_id, calories FROM dw.shipments_di WHERE employment_id > 287\"\n", "labels": {"reads": [{"table": "dw.shipments_di", "columns": ["employment_id", "collection_id", "publication_id", "calories"]}], "writes": [{"table": "civilcases", "columns": ["employment_id", "collection_id", "publication_id", "calories"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO collectivebargaining SELECT a.shipmentid, b.engagementid FROM product_details a JOIN courtcases b ON a.organization_id = b.organization_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "product_details", "columns": null}, {"table": "courtcases", "columns": null}], "writes": [{"table": "collectivebargaining", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO pediatricians SELECT incident_type, service, directed_by FROM news_stories WHERE incident_type > 185;\nEOF\n", "labels": {"reads": [{"table": "news_stories", "columns": ["incident_type", "service", "directed_by"]}], "writes": [{"table": "pediatricians", "columns": ["incident_type", "service", "directed_by"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO apartment_bookings SELECT asset_name, invoice_number FROM forests WHERE asset_name > 377\")\n", "labels": {"reads": [{"table": "forests", "columns": ["asset_name", "invoice_number"]}], "writes": [{"table": "apartment_bookings", "columns": ["asset_name", "invoice_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart.mart_member_point_df SELECT customer_email_address, well_depth, ll_hours, materialid FROM news_reporting WHERE customer_email_address > 38\"\n", "labels": {"reads": [{"table": "news_reporting", "columns": ["customer_email_address", "well_depth", "ll_hours", "materialid"]}], "writes": [{"table": "mart.mart_member_point_df", "columns": ["customer_email_address", "well_depth", "ll_hours", "materialid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM african_tourism\", conn)\ndf.to_sql(\"show\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "african_tourism", "columns": null}], "writes": [{"table": "show", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"az_drought_impact\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"dw.shipments_di\")\nsink_to_sink(df, \"ads_payments_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw.shipments_di", "columns": null}], "writes": [{"table": "ads_payments_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO beverages (item, num_attendees) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "beverages", "columns": ["item", "num_attendees"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"mineral_extraction\")\nsink_to_target(df, \"mining_companies\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": null}], "writes": [{"table": "mining_companies", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"canada_tech\")\npersist_to_store(df, \"policyholders\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "canada_tech", "columns": null}], "writes": [{"table": "policyholders", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM socially_responsible_loans\"\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.bi_inventory_full SELECT success, bdate FROM marine_species_indian WHERE success > 300\"\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": ["success", "bdate"]}], "writes": [{"table": "bi.bi_inventory_full", "columns": ["success", "bdate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.humidity > 92).all()\n# src table: school_bus\nengine.execute(\"INSERT INTO seeds SELECT * FROM school_bus\")\n", "labels": {"reads": [{"table": "school_bus", "columns": null}], "writes": [{"table": "seeds", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO mart.mart_coupon_use_full SELECT communityid, preference_rating, opponent_id, healthcareid FROM ref_budget_codes WHERE communityid > 196\"\n", "labels": {"reads": [{"table": "ref_budget_codes", "columns": ["communityid", "preference_rating", "opponent_id", "healthcareid"]}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": ["communityid", "preference_rating", "opponent_id", "healthcareid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO member_of SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO hotel_chains SELECT strainname, college_location FROM ads.member_point WHERE strainname > 45\"\n", "labels": {"reads": [{"table": "ads.member_point", "columns": ["strainname", "college_location"]}], "writes": [{"table": "hotel_chains", "columns": ["strainname", "college_location"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sportname, gas_production_2020 FROM mart.mart_coupon_use_delta LIMIT 132;\nINSERT INTO az_drought_impact SELECT total_donation_amount, park, rating_in_percent FROM fertilizer WHERE total_donation_amount > 421;\nSQL\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": ["sportname", "gas_production_2020"]}, {"table": "fertilizer", "columns": ["total_donation_amount", "park", "rating_in_percent"]}], "writes": [{"table": "az_drought_impact", "columns": ["total_donation_amount", "park", "rating_in_percent"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO clothingsales SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shoes depends on student_courses\ndbt run --select shoes --vars 'source: student_courses'\n", "labels": {"reads": [{"table": "student_courses", "columns": null}], "writes": [{"table": "shoes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT profits_billion, permit_number FROM immunizationrates\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"soccer_goals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "immunizationrates", "columns": ["profits_billion", "permit_number"]}], "writes": [{"table": "soccer_goals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO temperature_data SELECT product_stock_number, agency, budget_amount, q1_2022_views FROM legislation WHERE product_stock_number > 366\"\n", "labels": {"reads": [{"table": "legislation", "columns": ["product_stock_number", "agency", "budget_amount", "q1_2022_views"]}], "writes": [{"table": "temperature_data", "columns": ["product_stock_number", "agency", "budget_amount", "q1_2022_views"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO claims_processing_stages (pilot, service_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "claims_processing_stages", "columns": ["pilot", "service_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO esports_teams (size, local) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "esports_teams", "columns": ["size", "local"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bridgerainfall SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"restaurant_revenue\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT institution_name, contract_type FROM midwest_region\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"trenches\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "midwest_region", "columns": ["institution_name", "contract_type"]}], "writes": [{"table": "trenches", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO seafoodsouthafricakenya SELECT * FROM legacy\ncur.execute(\"SELECT detection_id, last_year FROM healthequitymetrics LIMIT 277\")\n", "labels": {"reads": [{"table": "healthequitymetrics", "columns": ["detection_id", "last_year"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table course_authors_and_tutors --columns steps,ll_activity --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "course_authors_and_tutors", "columns": ["steps", "ll_activity"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"all_programs\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"arctic_weather\")\n", "labels": {"reads": [{"table": "all_programs", "columns": null}], "writes": [{"table": "arctic_weather", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT is_compliant, volunteerdate FROM investment LIMIT 366;\nINSERT INTO ai_for_social_good SELECT tickets_sold, patient_id, case_type FROM whale_sightings WHERE tickets_sold > 111;\nSQL\n", "labels": {"reads": [{"table": "investment", "columns": ["is_compliant", "volunteerdate"]}, {"table": "whale_sightings", "columns": ["tickets_sold", "patient_id", "case_type"]}], "writes": [{"table": "ai_for_social_good", "columns": ["tickets_sold", "patient_id", "case_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO navalvessels SELECT is_accessible, installation_year FROM latam_schema.education_budget WHERE is_accessible > 115;\nEOF\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": ["is_accessible", "installation_year"]}], "writes": [{"table": "navalvessels", "columns": ["is_accessible", "installation_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT electoral_register_id, attraction_type_code FROM weather LIMIT 347;\nINSERT INTO affiliated_with SELECT restaurant, closure_authorised_by_staff_id, plant_name FROM ads.ads_exposure_daily WHERE restaurant > 493;\nSQL\n", "labels": {"reads": [{"table": "weather", "columns": ["electoral_register_id", "attraction_type_code"]}, {"table": "ads.ads_exposure_daily", "columns": ["restaurant", "closure_authorised_by_staff_id", "plant_name"]}], "writes": [{"table": "affiliated_with", "columns": ["restaurant", "closure_authorised_by_staff_id", "plant_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT meter_300, neighborhood FROM circular_economy LIMIT 139\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "circular_economy", "columns": ["meter_300", "neighborhood"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table music_database --target-dir /tmp/land\n", "labels": {"reads": [{"table": "music_database", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO crop_temperature SELECT a.fabricid, b.drug_id FROM goals a JOIN stores b ON a.vaccine_name = b.vaccine_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "goals", "columns": null}, {"table": "stores", "columns": null}], "writes": [{"table": "crop_temperature", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"mine_workforce\")\nsave_to_store(df, \"artsales\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mine_workforce", "columns": null}], "writes": [{"table": "artsales", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.volunteername > 218).all()\n# src table: classroom\nengine.execute(\"INSERT INTO platformstats SELECT * FROM classroom\")\n", "labels": {"reads": [{"table": "classroom", "columns": null}], "writes": [{"table": "platformstats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO languages SELECT * FROM legacy\ncur.execute(\"SELECT attack_date, record_id FROM sportsinfo LIMIT 426\")\n", "labels": {"reads": [{"table": "sportsinfo", "columns": ["attack_date", "record_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM trainings\", conn)\ndf.to_sql(\"waterusage\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "trainings", "columns": null}], "writes": [{"table": "waterusage", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table militaryinnovations --columns meter_100,occupancy_rate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "militaryinnovations", "columns": ["meter_100", "occupancy_rate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table food_justice_orgs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM prereq\", conn)\ndf.to_sql(\"events\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "prereq", "columns": null}], "writes": [{"table": "events", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT report_type, response_received_date FROM endowment LIMIT 339;\nINSERT INTO stg.cart_item_full SELECT attendee_id, bias_score, winning_aircraft FROM incarcerated WHERE attendee_id > 282;\nSQL\n", "labels": {"reads": [{"table": "endowment", "columns": ["report_type", "response_received_date"]}, {"table": "incarcerated", "columns": ["attendee_id", "bias_score", "winning_aircraft"]}], "writes": [{"table": "stg.cart_item_full", "columns": ["attendee_id", "bias_score", "winning_aircraft"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO veteran_employment SELECT occupancy_rate, cost_id, streamid, daily_hire_cost FROM safe_dataset WHERE occupancy_rate > 13\"\n", "labels": {"reads": [{"table": "safe_dataset", "columns": ["occupancy_rate", "cost_id", "streamid", "daily_hire_cost"]}], "writes": [{"table": "veteran_employment", "columns": ["occupancy_rate", "cost_id", "streamid", "daily_hire_cost"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO total_consumption SELECT hire_date, origin, treasurer_vote, num_projects FROM satellites_in_orbit WHERE hire_date > 450;\nEOF\n", "labels": {"reads": [{"table": "satellites_in_orbit", "columns": ["hire_date", "origin", "treasurer_vote", "num_projects"]}], "writes": [{"table": "total_consumption", "columns": ["hire_date", "origin", "treasurer_vote", "num_projects"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO evidence_based_policies SELECT a.personnelid, b.segment_id FROM immunization a JOIN stores_2 b ON a.supplier_name = b.supplier_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "immunization", "columns": null}, {"table": "stores_2", "columns": null}], "writes": [{"table": "evidence_based_policies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO road_construction SELECT temporary_acting, attendees FROM bi_shipments_daily WHERE temporary_acting > 253\")\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": ["temporary_acting", "attendees"]}], "writes": [{"table": "road_construction", "columns": ["temporary_acting", "attendees"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO humanitarian_assistance SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"shoes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"program_budget\")\n", "labels": {"reads": [{"table": "shoes", "columns": null}], "writes": [{"table": "program_budget", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO safetytestingcounts SELECT facid, eco_certified FROM ods.shipments_df WHERE facid > 489;\nEOF\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": ["facid", "eco_certified"]}], "writes": [{"table": "safetytestingcounts", "columns": ["facid", "eco_certified"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table restorative_justice_3 --columns cases_handled,device --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "restorative_justice_3", "columns": ["cases_handled", "device"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table player_award --target-dir /tmp/land\n", "labels": {"reads": [{"table": "player_award", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM veteran_unemployment\"\n", "labels": {"reads": [{"table": "veteran_unemployment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT amenid, diversity_score FROM vendorfabrics\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"food_items\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": ["amenid", "diversity_score"]}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO irrigation_systems SELECT network, bank_name FROM sustainable_menu_items WHERE network > 193\"\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": ["network", "bank_name"]}], "writes": [{"table": "irrigation_systems", "columns": ["network", "bank_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM assessment_notes\"\n", "labels": {"reads": [{"table": "assessment_notes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ai_ethics_policies SELECT container_id, violation_id, hashtags, coach_name FROM wins WHERE container_id > 271\")\n", "labels": {"reads": [{"table": "wins", "columns": ["container_id", "violation_id", "hashtags", "coach_name"]}], "writes": [{"table": "ai_ethics_policies", "columns": ["container_id", "violation_id", "hashtags", "coach_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM cargo_data\", conn)\ndf.to_sql(\"dw.dw_sessions_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "cargo_data", "columns": null}], "writes": [{"table": "dw.dw_sessions_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO florida_conservation_initiatives SELECT value_points, tournament_id, rainfall, countid FROM libraries WHERE value_points > 378\"], check=True)\n", "labels": {"reads": [{"table": "libraries", "columns": ["value_points", "tournament_id", "rainfall", "countid"]}], "writes": [{"table": "florida_conservation_initiatives", "columns": ["value_points", "tournament_id", "rainfall", "countid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO tencel_sources SELECT ssn, account_details, faculty FROM marine_species_indian WHERE ssn > 36;\nEOF\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": ["ssn", "account_details", "faculty"]}], "writes": [{"table": "tencel_sources", "columns": ["ssn", "account_details", "faculty"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dataset, observation_id FROM financial_capability LIMIT 235\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "financial_capability", "columns": ["dataset", "observation_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO taxi_data SELECT coowner_name, claim_status_description, city_area FROM dws.dws_events_df WHERE coowner_name > 430;\nEOF\n", "labels": {"reads": [{"table": "dws.dws_events_df", "columns": ["coowner_name", "claim_status_description", "city_area"]}], "writes": [{"table": "taxi_data", "columns": ["coowner_name", "claim_status_description", "city_area"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artcontributors\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"vehicle_sales\")\n", "labels": {"reads": [{"table": "artcontributors", "columns": null}], "writes": [{"table": "vehicle_sales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bustrips\").toPandas()\ndf[[\"apt_id\", \"reason\"]].to_sql(\"africa_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "bustrips", "columns": null}], "writes": [{"table": "africa_projects", "columns": ["apt_id", "reason"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mining_operation_data SELECT loan_id, election_cycle FROM community_policing_events WHERE loan_id > 38\"], check=True)\n", "labels": {"reads": [{"table": "community_policing_events", "columns": ["loan_id", "election_cycle"]}], "writes": [{"table": "mining_operation_data", "columns": ["loan_id", "election_cycle"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"architect\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"makeup_sales\")\n", "labels": {"reads": [{"table": "architect", "columns": null}], "writes": [{"table": "makeup_sales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO host SELECT a.expertise, b.host_city FROM artifactanalysis a JOIN disinformation_detection b ON a.quality_rank = b.quality_rank\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}, {"table": "disinformation_detection", "columns": null}], "writes": [{"table": "host", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mediterranean_salinity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO stg.stg_products_delta SELECT strain, party_name, detection_id FROM infra_diversification WHERE strain > 32\")\n", "labels": {"reads": [{"table": "infra_diversification", "columns": ["strain", "party_name", "detection_id"]}], "writes": [{"table": "stg.stg_products_delta", "columns": ["strain", "party_name", "detection_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM water_sources\", conn)\ndf.to_sql(\"restorative_justice_sentences\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "water_sources", "columns": null}], "writes": [{"table": "restorative_justice_sentences", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_exposure_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"aus_wellbeing\")\n", "labels": {"reads": [{"table": "ods_exposure_delta", "columns": null}], "writes": [{"table": "aus_wellbeing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"school_bus\")\nsrc.write.insertInto(\"gold\", overwrite=True)\n", "labels": {"reads": [{"table": "school_bus", "columns": null}], "writes": [{"table": "gold", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"articles_es\")\nexport_to_output(df, \"vr_tech\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "articles_es", "columns": null}], "writes": [{"table": "vr_tech", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT total, well_type FROM mobile_customers_global LIMIT 298\")\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ocean SELECT healthequitymetricscore, individual_last_name, functional_area_code FROM concert_sales WHERE healthequitymetricscore > 352\")\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": ["total", "well_type"]}, {"table": "concert_sales", "columns": ["healthequitymetricscore", "individual_last_name", "functional_area_code"]}], "writes": [{"table": "ocean", "columns": ["healthequitymetricscore", "individual_last_name", "functional_area_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"cargo_handling\")\nsave_to_store(df, \"social_issues\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "cargo_handling", "columns": null}], "writes": [{"table": "social_issues", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO document_locations SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT max_aperture, bikes_available FROM bias_categories LIMIT 227;\nINSERT INTO invoices SELECT co2_amount, restaurant_name, recycling_rate FROM dorm_amenity WHERE co2_amount > 186;\nSQL\n", "labels": {"reads": [{"table": "bias_categories", "columns": ["max_aperture", "bikes_available"]}, {"table": "dorm_amenity", "columns": ["co2_amount", "restaurant_name", "recycling_rate"]}], "writes": [{"table": "invoices", "columns": ["co2_amount", "restaurant_name", "recycling_rate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table skills --target-dir /tmp/land\n", "labels": {"reads": [{"table": "skills", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO financial_capability_id SELECT sustainability_rating, university_type, item_name FROM mart.mart_payments_delta WHERE sustainability_rating > 313\"\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": ["sustainability_rating", "university_type", "item_name"]}], "writes": [{"table": "financial_capability_id", "columns": ["sustainability_rating", "university_type", "item_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cultural_competency_program SELECT artworkid, town_city, round_date, ai_model FROM space_telescopes WHERE artworkid > 151\"\n", "labels": {"reads": [{"table": "space_telescopes", "columns": ["artworkid", "town_city", "round_date", "ai_model"]}], "writes": [{"table": "cultural_competency_program", "columns": ["artworkid", "town_city", "round_date", "ai_model"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO reservations SELECT a.base_name, b.industry FROM trains a JOIN repair_assignment b ON a.is_valid = b.is_valid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trains", "columns": null}, {"table": "repair_assignment", "columns": null}], "writes": [{"table": "reservations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT publisher, stream_id FROM fuel_consumption\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"publicchargingstations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fuel_consumption", "columns": ["publisher", "stream_id"]}], "writes": [{"table": "publicchargingstations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_diplomacy\").toPandas()\ndf[[\"continent_id\", \"budget_allocated\"]].to_sql(\"military_innovation\", engine, index=False)\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": null}], "writes": [{"table": "military_innovation", "columns": ["continent_id", "budget_allocated"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model workouts depends on dwd.events_daily\ndbt run --select workouts --vars '{\"src\":\"dwd.events_daily\"}'\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": null}], "writes": [{"table": "workouts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO game_sales SELECT coalquantity, ties, cargoid, team FROM drills WHERE coalquantity > 461\"\n", "labels": {"reads": [{"table": "drills", "columns": ["coalquantity", "ties", "cargoid", "team"]}], "writes": [{"table": "game_sales", "columns": ["coalquantity", "ties", "cargoid", "team"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT image_url, member_name FROM erc20_transactions LIMIT 245\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": ["image_url", "member_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT airport_id, sellingprice FROM state_budget LIMIT 161\")\nresult = value * ratio + offset\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO stg.stg_users_di SELECT yield, market_rate FROM researchers WHERE yield > 278\")\n", "labels": {"reads": [{"table": "state_budget", "columns": ["airport_id", "sellingprice"]}, {"table": "researchers", "columns": ["yield", "market_rate"]}], "writes": [{"table": "stg.stg_users_di", "columns": ["yield", "market_rate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cityname > 25).all()\n# src table: cybersecurity_vulnerabilities\nengine.execute(\"INSERT INTO inclusivehousingpolicies SELECT * FROM cybersecurity_vulnerabilities\")\n", "labels": {"reads": [{"table": "cybersecurity_vulnerabilities", "columns": null}], "writes": [{"table": "inclusivehousingpolicies", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO branch SELECT facid, hiv, population, booking_status_code FROM canals WHERE facid > 443\"\n", "labels": {"reads": [{"table": "canals", "columns": ["facid", "hiv", "population", "booking_status_code"]}], "writes": [{"table": "branch", "columns": ["facid", "hiv", "population", "booking_status_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"na_schema.hospitals\").toPandas()\ndf[[\"contractor_name\", \"contract_number\"]].to_sql(\"crime_incidents\", engine, index=False)\n", "labels": {"reads": [{"table": "na_schema.hospitals", "columns": null}], "writes": [{"table": "crime_incidents", "columns": ["contractor_name", "contract_number"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model usdaviolations depends on fault_log\ndbt build --select usdaviolations --vars 'source: fault_log'\n", "labels": {"reads": [{"table": "fault_log", "columns": null}], "writes": [{"table": "usdaviolations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO org_volunteer (unsure_rate, scooter_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "org_volunteer", "columns": ["unsure_rate", "scooter_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_exposure_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"agriculturalinvestments\")\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": null}], "writes": [{"table": "agriculturalinvestments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.production_qty > 189).all()\n# src table: financial_transactions\nengine.execute(\"INSERT INTO member SELECT * FROM financial_transactions\")\n", "labels": {"reads": [{"table": "financial_transactions", "columns": null}], "writes": [{"table": "member", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_campaigns_daily --columns diversity_score,marketing_region_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["diversity_score", "marketing_region_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.school > 113).all()\n# src table: province.human_rights_data\nengine.execute(\"INSERT INTO city_department SELECT * FROM province.human_rights_data\")\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": null}], "writes": [{"table": "city_department", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO waste_management_projects SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recycled_polyester\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"red_line\")\n", "labels": {"reads": [{"table": "recycled_polyester", "columns": null}], "writes": [{"table": "red_line", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"yoga\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ods.inventory_df\")\n", "labels": {"reads": [{"table": "yoga", "columns": null}], "writes": [{"table": "ods.inventory_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO ods.ods_sessions_df SELECT a.sportname, b.method_name FROM person a JOIN ads.vendors_delta b ON a.investment = b.investment\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "person", "columns": null}, {"table": "ads.vendors_delta", "columns": null}], "writes": [{"table": "ods.ods_sessions_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table savings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "savings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table communitycourtcases --columns permits_issued,fund_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "communitycourtcases", "columns": ["permits_issued", "fund_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT flight_number, dept_name FROM sustainablebrands LIMIT 250\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "sustainablebrands", "columns": ["flight_number", "dept_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO facility_production (union_id, sqft) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "facility_production", "columns": ["union_id", "sqft"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO game_sessions SELECT start_date, dates_active FROM community_health_center WHERE start_date > 327\"\n", "labels": {"reads": [{"table": "community_health_center", "columns": ["start_date", "dates_active"]}], "writes": [{"table": "game_sessions", "columns": ["start_date", "dates_active"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT profession_count, cinema_id FROM exhibitionattendance LIMIT 334\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "exhibitionattendance", "columns": ["profession_count", "cinema_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"esportsevents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "esportsevents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM species_observations\"\n", "labels": {"reads": [{"table": "species_observations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model student_tests_taken depends on clothingitems\ndbt run --select student_tests_taken --vars '{\"src\":\"clothingitems\"}'\n", "labels": {"reads": [{"table": "clothingitems", "columns": null}], "writes": [{"table": "student_tests_taken", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO resource_extraction SELECT a.school_id, b.extraction_date FROM stg.campaigns_daily a JOIN bi_products b ON a.policy_holder_id = b.policy_holder_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.campaigns_daily", "columns": null}, {"table": "bi_products", "columns": null}], "writes": [{"table": "resource_extraction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model staff_members depends on tourism_activities\ndbt build --models staff_members --vars '{\"source_table\":\"tourism_activities\"}'\n", "labels": {"reads": [{"table": "tourism_activities", "columns": null}], "writes": [{"table": "staff_members", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bike_station_info SELECT * FROM legacy\ncur.execute(\"SELECT price_in_dollars, mission_date FROM gene LIMIT 222\")\n", "labels": {"reads": [{"table": "gene", "columns": ["price_in_dollars", "mission_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart_vendors_full SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT coverage_type, performancedate FROM news_views\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"eco_diversification_investment\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "news_views", "columns": ["coverage_type", "performancedate"]}], "writes": [{"table": "eco_diversification_investment", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.member_point_df SELECT * FROM legacy\ncur.execute(\"SELECT numcases, time_day FROM production_data LIMIT 493\")\n", "labels": {"reads": [{"table": "production_data", "columns": ["numcases", "time_day"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO dw.inventory_delta SELECT diagnosis, asset_make, approach, accessible FROM degrees WHERE diagnosis > 433\"\n", "labels": {"reads": [{"table": "degrees", "columns": ["diagnosis", "asset_make", "approach", "accessible"]}], "writes": [{"table": "dw.inventory_delta", "columns": ["diagnosis", "asset_make", "approach", "accessible"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO trains SELECT a.vegan, b.completed FROM conservation_initiatives a JOIN mart.mart_coupon_use_full b ON a.track_id = b.track_id\"\n", "labels": {"reads": [{"table": "conservation_initiatives", "columns": null}, {"table": "mart.mart_coupon_use_full", "columns": null}], "writes": [{"table": "trains", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT investmentid, retweets FROM farmers LIMIT 88;\nINSERT INTO epl_teams SELECT spacecraft_name, quantity_containers, hourdate, organisation_type FROM wastewater_treatment WHERE spacecraft_name > 402;\nSQL\n", "labels": {"reads": [{"table": "farmers", "columns": ["investmentid", "retweets"]}, {"table": "wastewater_treatment", "columns": ["spacecraft_name", "quantity_containers", "hourdate", "organisation_type"]}], "writes": [{"table": "epl_teams", "columns": ["spacecraft_name", "quantity_containers", "hourdate", "organisation_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO heart_rate_data SELECT crime_type, studentid, funding_round_id FROM sustainable_building WHERE crime_type > 152\")\n", "labels": {"reads": [{"table": "sustainable_building", "columns": ["crime_type", "studentid", "funding_round_id"]}], "writes": [{"table": "heart_rate_data", "columns": ["crime_type", "studentid", "funding_round_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"soccer_goals\")\nwrite_to_sink(df, \"customer_contact_channels\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "soccer_goals", "columns": null}], "writes": [{"table": "customer_contact_channels", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO workers SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO rural_resources SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO marine_life_data SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table europium_exports --columns form_name,date_opened --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "europium_exports", "columns": ["form_name", "date_opened"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO productsafety SELECT stu_hrs, singer_id FROM community_health_center WHERE stu_hrs > 212\"], check=True)\n", "labels": {"reads": [{"table": "community_health_center", "columns": ["stu_hrs", "singer_id"]}], "writes": [{"table": "productsafety", "columns": ["stu_hrs", "singer_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO budget SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO aircraft (personnel_id, mean_sea_level_pressure_inches) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "aircraft", "columns": ["personnel_id", "mean_sea_level_pressure_inches"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_contracts_v2\").toPandas()\ndf[[\"aid_id\", \"writer\"]].to_sql(\"incarcerated\", engine, index=False)\n", "labels": {"reads": [{"table": "defense_contracts_v2", "columns": null}], "writes": [{"table": "incarcerated", "columns": ["aid_id", "writer"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO rent_arrears SELECT num_volunteers, class_president_vote FROM pilot_record WHERE num_volunteers > 180\"\n", "labels": {"reads": [{"table": "pilot_record", "columns": ["num_volunteers", "class_president_vote"]}], "writes": [{"table": "rent_arrears", "columns": ["num_volunteers", "class_president_vote"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO products SELECT a.extraction_amount, b.lesson_status_code FROM passenger_trips a JOIN agriculturalinnovations b ON a.order_id = b.order_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "passenger_trips", "columns": null}, {"table": "agriculturalinnovations", "columns": null}], "writes": [{"table": "products", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM shipment_data\", conn)\ndf.to_sql(\"safetyincidents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "shipment_data", "columns": null}], "writes": [{"table": "safetyincidents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT schedule_date, opening_year FROM screen_mode\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"electric_vehicles\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "screen_mode", "columns": ["schedule_date", "opening_year"]}], "writes": [{"table": "electric_vehicles", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT grant_amount, likes FROM students_lifelong_learning LIMIT 94\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO department_publications SELECT number_of_vessels, coownerid FROM property_community WHERE number_of_vessels > 476\")\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["grant_amount", "likes"]}, {"table": "property_community", "columns": ["number_of_vessels", "coownerid"]}], "writes": [{"table": "department_publications", "columns": ["number_of_vessels", "coownerid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model stg_risk_score_df depends on public_participation\ndbt run --models stg_risk_score_df --vars 'source: public_participation'\n", "labels": {"reads": [{"table": "public_participation", "columns": null}], "writes": [{"table": "stg_risk_score_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO producersnewmexico (num_cases, domestic_passengers) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "producersnewmexico", "columns": ["num_cases", "domestic_passengers"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO ship_agent SELECT other_characteristic_details, transact_count FROM arctic_marine_species WHERE other_characteristic_details > 408\"\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": ["other_characteristic_details", "transact_count"]}], "writes": [{"table": "ship_agent", "columns": ["other_characteristic_details", "transact_count"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO train_station SELECT farm_id, chemical_name, fouls, policyholderid FROM election WHERE farm_id > 262\")\n", "labels": {"reads": [{"table": "election", "columns": ["farm_id", "chemical_name", "fouls", "policyholderid"]}], "writes": [{"table": "train_station", "columns": ["farm_id", "chemical_name", "fouls", "policyholderid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO inmates SELECT total_passengers, clubname FROM fabricdata WHERE total_passengers > 405\"\n", "labels": {"reads": [{"table": "fabricdata", "columns": ["total_passengers", "clubname"]}], "writes": [{"table": "inmates", "columns": ["total_passengers", "clubname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO industrial_customers SELECT channel, relationship, station_name, institution_id FROM overwatch_scores WHERE channel > 314\")\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": ["channel", "relationship", "station_name", "institution_id"]}], "writes": [{"table": "industrial_customers", "columns": ["channel", "relationship", "station_name", "institution_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO coowners SELECT a.production_rate, b.state_id FROM education_union a JOIN government.region b ON a.crs_credit = b.crs_credit\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "education_union", "columns": null}, {"table": "government.region", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO safety_incidents SELECT join_date, stu_lname, event_id, driverid FROM defense_spending WHERE join_date > 44\"\n", "labels": {"reads": [{"table": "defense_spending", "columns": ["join_date", "stu_lname", "event_id", "driverid"]}], "writes": [{"table": "safety_incidents", "columns": ["join_date", "stu_lname", "event_id", "driverid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO hospital_visits SELECT num_of_shops, sent_date, artifact_name, offset_id FROM ref_service_types WHERE num_of_shops > 49\")\n", "labels": {"reads": [{"table": "ref_service_types", "columns": ["num_of_shops", "sent_date", "artifact_name", "offset_id"]}], "writes": [{"table": "hospital_visits", "columns": ["num_of_shops", "sent_date", "artifact_name", "offset_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model project_timeline depends on zip_codes\ndbt run -s project_timeline --vars '{\"source_table\":\"zip_codes\"}'\n", "labels": {"reads": [{"table": "zip_codes", "columns": null}], "writes": [{"table": "project_timeline", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO flights SELECT a.gamepreference, b.game FROM dw.shipments_df a JOIN archaeologists b ON a.post_date = b.post_date\"\n", "labels": {"reads": [{"table": "dw.shipments_df", "columns": null}, {"table": "archaeologists", "columns": null}], "writes": [{"table": "flights", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO orgdonations SELECT consultations, ota_id FROM invoice_lines WHERE consultations > 359\"\n", "labels": {"reads": [{"table": "invoice_lines", "columns": ["consultations", "ota_id"]}], "writes": [{"table": "orgdonations", "columns": ["consultations", "ota_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO explainableai (product_stock_number, game) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "explainableai", "columns": ["product_stock_number", "game"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT name_full, check_in_id FROM farmers_india\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"concert_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "farmers_india", "columns": ["name_full", "check_in_id"]}], "writes": [{"table": "concert_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO list SELECT engagement_date, customer_email_address, annual_revenue FROM cosmetics.lipstick_spf_data WHERE engagement_date > 200\"\n", "labels": {"reads": [{"table": "cosmetics.lipstick_spf_data", "columns": ["engagement_date", "customer_email_address", "annual_revenue"]}], "writes": [{"table": "list", "columns": ["engagement_date", "customer_email_address", "annual_revenue"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_contact_to, build_date FROM vesselfuel\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"cycling\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "vesselfuel", "columns": ["date_contact_to", "build_date"]}], "writes": [{"table": "cycling", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"districts_india\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"item_prices\")\n", "labels": {"reads": [{"table": "districts_india", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ods.shipments_df SELECT drugname, acc_bal, initiative_type FROM communitypolicing WHERE drugname > 422\"\n", "labels": {"reads": [{"table": "communitypolicing", "columns": ["drugname", "acc_bal", "initiative_type"]}], "writes": [{"table": "ods.shipments_df", "columns": ["drugname", "acc_bal", "initiative_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT college_location, musical_id FROM field_production LIMIT 453\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO city_waste_generation SELECT total_attendance, country_id, producerid, quantityproduced FROM daily_oil_production WHERE total_attendance > 253\")\n", "labels": {"reads": [{"table": "field_production", "columns": ["college_location", "musical_id"]}, {"table": "daily_oil_production", "columns": ["total_attendance", "country_id", "producerid", "quantityproduced"]}], "writes": [{"table": "city_waste_generation", "columns": ["total_attendance", "country_id", "producerid", "quantityproduced"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO student_access SELECT inventor_name, strategy_id FROM sales_region WHERE inventor_name > 33\")\n", "labels": {"reads": [{"table": "sales_region", "columns": ["inventor_name", "strategy_id"]}], "writes": [{"table": "student_access", "columns": ["inventor_name", "strategy_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"defenseprojects\")\npersist_to_output(df, \"education_union\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}], "writes": [{"table": "education_union", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.clean_jerk > 396).all()\n# src table: mines\nengine.execute(\"INSERT INTO prescribes SELECT * FROM mines\")\n", "labels": {"reads": [{"table": "mines", "columns": null}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM audience\"\n", "labels": {"reads": [{"table": "audience", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.bi_orders_daily SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ingredientsvegancrueltyfree SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO artcollection SELECT co2_emission, content_type FROM drug_sales WHERE co2_emission > 311\"], check=True)\n", "labels": {"reads": [{"table": "drug_sales", "columns": ["co2_emission", "content_type"]}], "writes": [{"table": "artcollection", "columns": ["co2_emission", "content_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM solar_farms\"\n", "labels": {"reads": [{"table": "solar_farms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO state_energy SELECT quantityproduced, emission_date, menu_name, ad_type FROM crime_stats WHERE quantityproduced > 392\")\n", "labels": {"reads": [{"table": "crime_stats", "columns": ["quantityproduced", "emission_date", "menu_name", "ad_type"]}], "writes": [{"table": "state_energy", "columns": ["quantityproduced", "emission_date", "menu_name", "ad_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO useracct SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.product_details > 222).all()\n# src table: community_events\nengine.execute(\"INSERT INTO dw.users_hourly SELECT * FROM community_events\")\n", "labels": {"reads": [{"table": "community_events", "columns": null}], "writes": [{"table": "dw.users_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO diversity SELECT role_name, operationname, trial_name, sitename FROM riskassessments WHERE role_name > 69\"\n", "labels": {"reads": [{"table": "riskassessments", "columns": ["role_name", "operationname", "trial_name", "sitename"]}], "writes": [{"table": "diversity", "columns": ["role_name", "operationname", "trial_name", "sitename"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nba_games SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"dwd_sessions_hourly\")\ndump_to_warehouse(df, \"dwd.dwd_campaigns\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": null}], "writes": [{"table": "dwd.dwd_campaigns", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO mediatype SELECT low_estimate, percentage_change, opid FROM shariah_financing WHERE low_estimate > 32\")\n", "labels": {"reads": [{"table": "shariah_financing", "columns": ["low_estimate", "percentage_change", "opid"]}], "writes": [{"table": "mediatype", "columns": ["low_estimate", "percentage_change", "opid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO vessels SELECT handling_id, factory FROM brandrevenue WHERE handling_id > 482\")\n", "labels": {"reads": [{"table": "brandrevenue", "columns": ["handling_id", "factory"]}], "writes": [{"table": "vessels", "columns": ["handling_id", "factory"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO playergamedata (faculty, founder_gender) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "playergamedata", "columns": ["faculty", "founder_gender"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donationyear, volunteer_quarter FROM mart.mart_products_hourly LIMIT 412\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": ["donationyear", "volunteer_quarter"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainable_projects SELECT * FROM legacy\ncur.execute(\"SELECT billingid, inclusive_housing_policy FROM military_equipment LIMIT 199\")\n", "labels": {"reads": [{"table": "military_equipment", "columns": ["billingid", "inclusive_housing_policy"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT pilot_id, crop_name FROM accessible_tech_categories LIMIT 459\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "accessible_tech_categories", "columns": ["pilot_id", "crop_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model songs depends on latam_schema.education_budget\ndbt build -s songs --vars 'source: latam_schema.education_budget'\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO artwork_styles (headquarter, process_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "artwork_styles", "columns": ["headquarter", "process_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"communities\")\ndump_to_output(df, \"supplier_ethics\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": [{"table": "supplier_ethics", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"platformg\")\npersist_to_output(df, \"all_star\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "platformg", "columns": null}], "writes": [{"table": "all_star", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO advisor SELECT a.yield_per_acre, b.purchase_details FROM unesco_intangible_heritage a JOIN electricvehiclestats b ON a.airport_id = b.airport_id\"\n", "labels": {"reads": [{"table": "unesco_intangible_heritage", "columns": null}, {"table": "electricvehiclestats", "columns": null}], "writes": [{"table": "advisor", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dwd.dwd_events_delta SELECT production_rate, dname FROM activities WHERE production_rate > 427;\nEOF\n", "labels": {"reads": [{"table": "activities", "columns": ["production_rate", "dname"]}], "writes": [{"table": "dwd.dwd_events_delta", "columns": ["production_rate", "dname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sustainability SELECT partnership_id, dispensary_name FROM fraud_detections WHERE partnership_id > 318;\nEOF\n", "labels": {"reads": [{"table": "fraud_detections", "columns": ["partnership_id", "dispensary_name"]}], "writes": [{"table": "sustainability", "columns": ["partnership_id", "dispensary_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"public.forest_stats\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"spacecraft_components\")\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": null}], "writes": [{"table": "spacecraft_components", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT pilot_name, visit_month FROM hockey_players LIMIT 6\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "hockey_players", "columns": ["pilot_name", "visit_month"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO infrastructure SELECT race_ethnicity_id, aid_name, num_pallets FROM state_energy WHERE race_ethnicity_id > 116\"\n", "labels": {"reads": [{"table": "state_energy", "columns": ["race_ethnicity_id", "aid_name", "num_pallets"]}], "writes": [{"table": "infrastructure", "columns": ["race_ethnicity_id", "aid_name", "num_pallets"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_waste_generation\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"threatintelligence\")\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": null}], "writes": [{"table": "threatintelligence", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO warehouses (has_spf, electoral_register_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "warehouses", "columns": ["has_spf", "electoral_register_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT financially_capable, rental_rate FROM certifications LIMIT 213\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "certifications", "columns": ["financially_capable", "rental_rate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"postseason\").toPandas()\ndf[[\"market_rate\", \"attraction_type_description\"]].to_sql(\"healthydelights\", engine, index=False)\n", "labels": {"reads": [{"table": "postseason", "columns": null}], "writes": [{"table": "healthydelights", "columns": ["market_rate", "attraction_type_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO highways SELECT operationname, date_payment_made FROM contract_negotiations_un WHERE operationname > 340;\nEOF\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": ["operationname", "date_payment_made"]}], "writes": [{"table": "highways", "columns": ["operationname", "date_payment_made"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM public.crime_types\"\n", "labels": {"reads": [{"table": "public.crime_types", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.inventory_hourly --columns workers,account_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.inventory_hourly", "columns": ["workers", "account_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM streams\"\n", "labels": {"reads": [{"table": "streams", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"education_programs\")\nsave_to_sink(df, \"measurements\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "education_programs", "columns": null}], "writes": [{"table": "measurements", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.rental_rate > 383).all()\n# src table: singer\nengine.execute(\"INSERT INTO socially_responsible_lending SELECT * FROM singer\")\n", "labels": {"reads": [{"table": "singer", "columns": null}], "writes": [{"table": "socially_responsible_lending", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table engineer_visits --columns device_id,saleid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "engineer_visits", "columns": ["device_id", "saleid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vrusers\", conn)\ndf.to_sql(\"textile_suppliers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vrusers", "columns": null}], "writes": [{"table": "textile_suppliers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"contractnegotiations\").toPandas()\ndf[[\"servicename\", \"conferencename\"]].to_sql(\"audience_demographics\", engine, index=False)\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": null}], "writes": [{"table": "audience_demographics", "columns": ["servicename", "conferencename"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_incident_end, safety_record FROM academic_publications\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"tickets_3\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["date_incident_end", "safety_record"]}], "writes": [{"table": "tickets_3", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT building_short_name, draft_details FROM daily_production LIMIT 259\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "daily_production", "columns": ["building_short_name", "draft_details"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vrusers SELECT state_county, employees, fairness_score, serviceid FROM cultural_events WHERE state_county > 481\"\n", "labels": {"reads": [{"table": "cultural_events", "columns": ["state_county", "employees", "fairness_score", "serviceid"]}], "writes": [{"table": "vrusers", "columns": ["state_county", "employees", "fairness_score", "serviceid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM broadband_providers\", conn)\ndf.to_sql(\"daily_revenue\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "broadband_providers", "columns": null}], "writes": [{"table": "daily_revenue", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO category_revenue (investor_name, seating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "category_revenue", "columns": ["investor_name", "seating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attorneylocationyear\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO guests SELECT workoutdate, source FROM dorm_amenity WHERE workoutdate > 359\"\n", "labels": {"reads": [{"table": "dorm_amenity", "columns": ["workoutdate", "source"]}], "writes": [{"table": "guests", "columns": ["workoutdate", "source"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.billing_state > 342).all()\n# src table: exhibition_record\nengine.execute(\"INSERT INTO events SELECT * FROM exhibition_record\")\n", "labels": {"reads": [{"table": "exhibition_record", "columns": null}], "writes": [{"table": "events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO researchprojects SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO satellite_missions_large SELECT home_games, is_electric, rural_area FROM mart.mart_member_point_df WHERE home_games > 31\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": ["home_games", "is_electric", "rural_area"]}], "writes": [{"table": "satellite_missions_large", "columns": ["home_games", "is_electric", "rural_area"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM runs\", conn)\ndf.to_sql(\"threat_intel\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": [{"table": "threat_intel", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"bus_routes\")\nupsert_to_target(df, \"game_sales\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}], "writes": [{"table": "game_sales", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mine_workforce SELECT playerid, routename, park_id FROM languagesatrisk WHERE playerid > 8\"\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": ["playerid", "routename", "park_id"]}], "writes": [{"table": "mine_workforce", "columns": ["playerid", "routename", "park_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO seamounts SELECT a.artist_gender, b.vessel_id FROM dws.cart_item_full a JOIN trends_2022 b ON a.donor = b.donor\"\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": null}, {"table": "trends_2022", "columns": null}], "writes": [{"table": "seamounts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO research_vessels SELECT last_used_bus, hospital_name FROM weather_record WHERE last_used_bus > 110\"\n", "labels": {"reads": [{"table": "weather_record", "columns": ["last_used_bus", "hospital_name"]}], "writes": [{"table": "research_vessels", "columns": ["last_used_bus", "hospital_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"vocals\")\npush_to_target(df, \"humanitarian_aid\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vocals", "columns": null}], "writes": [{"table": "humanitarian_aid", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"astronaut_medical_3\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.dws_orders SELECT dance_form, ei_value, stat_type, virtual_tour_engagement_time FROM jupiter_missions WHERE dance_form > 131\"], check=True)\n", "labels": {"reads": [{"table": "jupiter_missions", "columns": ["dance_form", "ei_value", "stat_type", "virtual_tour_engagement_time"]}], "writes": [{"table": "dws.dws_orders", "columns": ["dance_form", "ei_value", "stat_type", "virtual_tour_engagement_time"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO restorative_justice_programs SELECT restaurant, technician_id, transact_count FROM chip_model WHERE restaurant > 117\"\n", "labels": {"reads": [{"table": "chip_model", "columns": ["restaurant", "technician_id", "transact_count"]}], "writes": [{"table": "restorative_justice_programs", "columns": ["restaurant", "technician_id", "transact_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"shariah_compliant_finance\")\nexport_to_sink(df, \"event_attendance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "shariah_compliant_finance", "columns": null}], "writes": [{"table": "event_attendance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO building SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO veteran_occupations SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO carbon_prices_3 SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO financial_capability_programs SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"wind_energy\")\nsrc.write.insertInto(\"workersalaries\", overwrite=True)\n", "labels": {"reads": [{"table": "wind_energy", "columns": null}], "writes": [{"table": "workersalaries", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model engineering_projects depends on wind_turbines\ndbt run -s engineering_projects --vars '{\"source_table\":\"wind_turbines\"}'\n", "labels": {"reads": [{"table": "wind_turbines", "columns": null}], "writes": [{"table": "engineering_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO participation SELECT founder_country, patient_id FROM contract_states WHERE founder_country > 174\"\n", "labels": {"reads": [{"table": "contract_states", "columns": ["founder_country", "patient_id"]}], "writes": [{"table": "participation", "columns": ["founder_country", "patient_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO noise_pollution SELECT a.sessionid, b.cost_id FROM paintings a JOIN ads.payments_di b ON a.vessel = b.vessel\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "paintings", "columns": null}, {"table": "ads.payments_di", "columns": null}], "writes": [{"table": "noise_pollution", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.inventory_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"disabilityadvocacy\")\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": null}], "writes": [{"table": "disabilityadvocacy", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO genre SELECT date_payment_made, video_id FROM creative_ai WHERE date_payment_made > 95\"\n", "labels": {"reads": [{"table": "creative_ai", "columns": ["date_payment_made", "video_id"]}], "writes": [{"table": "genre", "columns": ["date_payment_made", "video_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO energy_storage SELECT a.materialtype, b.observation_date FROM part_faults a JOIN dwd.dwd_payments_full b ON a.carbon_offset_tons = b.carbon_offset_tons\"\n", "labels": {"reads": [{"table": "part_faults", "columns": null}, {"table": "dwd.dwd_payments_full", "columns": null}], "writes": [{"table": "energy_storage", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT aid_id, frequency FROM cultural_competency_training\", engine)\nif not rows:\n    logger.warning('empty result')\nimport logging\ndf.to_sql(\"ods_shipments_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": ["aid_id", "frequency"]}], "writes": [{"table": "ods_shipments_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO legal_aid_providers SELECT * FROM legacy\ncur.execute(\"SELECT virtual_tour_views, warehouseid FROM housing_investments LIMIT 492\")\n", "labels": {"reads": [{"table": "housing_investments", "columns": ["virtual_tour_views", "warehouseid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table research_vessels --target-dir /tmp/land\n", "labels": {"reads": [{"table": "research_vessels", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.dw_member_point_hourly (fan_age, debate_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_member_point_hourly", "columns": ["fan_age", "debate_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO noise_pollution SELECT vehicle, credit_score, reported FROM development_hours WHERE vehicle > 400\"\n", "labels": {"reads": [{"table": "development_hours", "columns": ["vehicle", "credit_score", "reported"]}], "writes": [{"table": "noise_pollution", "columns": ["vehicle", "credit_score", "reported"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"perpetrator\")\ndump_to_warehouse(df, \"multimodal_trips\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "perpetrator", "columns": null}], "writes": [{"table": "multimodal_trips", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_risk_score_delta --columns rebounds,shipment_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["rebounds", "shipment_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO auctions SELECT a.form_type_code, b.is_dessert FROM community_health_center a JOIN shariah_compliant_products b ON a.teacher_id = b.teacher_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "community_health_center", "columns": null}, {"table": "shariah_compliant_products", "columns": null}], "writes": [{"table": "auctions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO aquaculture_farms SELECT goal_id, investors, student_capacity, city_name FROM florida_conservation_initiatives WHERE goal_id > 394\"], check=True)\n", "labels": {"reads": [{"table": "florida_conservation_initiatives", "columns": ["goal_id", "investors", "student_capacity", "city_name"]}], "writes": [{"table": "aquaculture_farms", "columns": ["goal_id", "investors", "student_capacity", "city_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO climate_projects SELECT vin, hotel_chain_id, visitors FROM product_review WHERE vin > 404\"\n", "labels": {"reads": [{"table": "product_review", "columns": ["vin", "hotel_chain_id", "visitors"]}], "writes": [{"table": "climate_projects", "columns": ["vin", "hotel_chain_id", "visitors"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT post_date, store_id FROM animals LIMIT 485\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "animals", "columns": ["post_date", "store_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods.ods_campaigns_delta --columns onscholarship,species_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_campaigns_delta", "columns": ["onscholarship", "species_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"co2price\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"initiative_types\")\n", "labels": {"reads": [{"table": "co2price", "columns": null}], "writes": [{"table": "initiative_types", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT operation_id, node_id FROM sustainable_projects LIMIT 87;\nINSERT INTO gamedesign SELECT retailer_id, date_test_taken FROM first_notification_of_loss WHERE retailer_id > 124;\nSQL\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["operation_id", "node_id"]}, {"table": "first_notification_of_loss", "columns": ["retailer_id", "date_test_taken"]}], "writes": [{"table": "gamedesign", "columns": ["retailer_id", "date_test_taken"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"storage\")\nsrc.write.insertInto(\"projecttimeline\", overwrite=True)\n", "labels": {"reads": [{"table": "storage", "columns": null}], "writes": [{"table": "projecttimeline", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT startyear, long FROM dwd.dwd_products LIMIT 391\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO geologicalsurvey SELECT grantamount, attorneyid FROM ngo_funding WHERE grantamount > 47\")\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": ["startyear", "long"]}, {"table": "ngo_funding", "columns": ["grantamount", "attorneyid"]}], "writes": [{"table": "geologicalsurvey", "columns": ["grantamount", "attorneyid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ecohousing\")\nsrc.write.insertInto(\"constructionlaborstatistics\", overwrite=True)\n", "labels": {"reads": [{"table": "ecohousing", "columns": null}], "writes": [{"table": "constructionlaborstatistics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dp_articles SELECT a.operationid, b.asset_id FROM workforce_development_programs a JOIN safety_research b ON a.impressions = b.impressions\"\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": null}, {"table": "safety_research", "columns": null}], "writes": [{"table": "dp_articles", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO sitem (store_id, is_recycled) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "sitem", "columns": ["store_id", "is_recycled"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shariah_financing (activity, access_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shariah_financing", "columns": ["activity", "access_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO camera_lens SELECT date_problem_reported, open_date, authorder FROM department_stores WHERE date_problem_reported > 46\"\n", "labels": {"reads": [{"table": "department_stores", "columns": ["date_problem_reported", "open_date", "authorder"]}], "writes": [{"table": "camera_lens", "columns": ["date_problem_reported", "open_date", "authorder"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"draft_copies\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO operations (albumname, characteristic_type_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "operations", "columns": ["albumname", "characteristic_type_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO policyadvocacyevents (investmentid, fund_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "policyadvocacyevents", "columns": ["investmentid", "fund_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.exposure SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT nurse, ei_category FROM takes LIMIT 137\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ads.inventory_di SELECT trial_year, invoice_date, emp_lname, color_description FROM legal_technology_funding WHERE trial_year > 246\")\n", "labels": {"reads": [{"table": "takes", "columns": ["nurse", "ei_category"]}, {"table": "legal_technology_funding", "columns": ["trial_year", "invoice_date", "emp_lname", "color_description"]}], "writes": [{"table": "ads.inventory_di", "columns": ["trial_year", "invoice_date", "emp_lname", "color_description"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO customer_transactions SELECT socialimpactscore, department_id FROM fuel_consumption WHERE socialimpactscore > 358\"\n", "labels": {"reads": [{"table": "fuel_consumption", "columns": ["socialimpactscore", "department_id"]}], "writes": [{"table": "customer_transactions", "columns": ["socialimpactscore", "department_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model thefts depends on user_reactions\ndbt build --select thefts --vars '{\"source_table\":\"user_reactions\"}'\n", "labels": {"reads": [{"table": "user_reactions", "columns": null}], "writes": [{"table": "thefts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM recreation_centers\", conn)\ndf.to_sql(\"climateresearch\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "recreation_centers", "columns": null}], "writes": [{"table": "climateresearch", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.coupon_use\"\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT gamegenre, order_item_status FROM dwd.dwd_cart_item_di LIMIT 318\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO artsales SELECT itemname, algorithmic_fairness_score FROM peacekeepingmissions WHERE itemname > 7\")\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": ["gamegenre", "order_item_status"]}, {"table": "peacekeepingmissions", "columns": ["itemname", "algorithmic_fairness_score"]}], "writes": [{"table": "artsales", "columns": ["itemname", "algorithmic_fairness_score"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table onlineengagement --target-dir /tmp/land\n", "labels": {"reads": [{"table": "onlineengagement", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO ads.ads_orders_full SELECT task_details, program_category, retailer, production FROM participation WHERE task_details > 413\"\n", "labels": {"reads": [{"table": "participation", "columns": ["task_details", "program_category", "retailer", "production"]}], "writes": [{"table": "ads.ads_orders_full", "columns": ["task_details", "program_category", "retailer", "production"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO space_exploration SELECT nationality, coalquantity, funding_id, time_second FROM bi.bi_inventory_full WHERE nationality > 450;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": ["nationality", "coalquantity", "funding_id", "time_second"]}], "writes": [{"table": "space_exploration", "columns": ["nationality", "coalquantity", "funding_id", "time_second"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO circular_economy SELECT unionid, num_owners FROM clinics WHERE unionid > 262\"\n", "labels": {"reads": [{"table": "clinics", "columns": ["unionid", "num_owners"]}], "writes": [{"table": "circular_economy", "columns": ["unionid", "num_owners"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT energy_production, stadium_id FROM exoplanet_discoveries LIMIT 234;\nINSERT INTO restorative_justice_sentences SELECT neighborhood, count, clinic_type FROM mineral_extraction_us WHERE neighborhood > 93;\nSQL\n", "labels": {"reads": [{"table": "exoplanet_discoveries", "columns": ["energy_production", "stadium_id"]}, {"table": "mineral_extraction_us", "columns": ["neighborhood", "count", "clinic_type"]}], "writes": [{"table": "restorative_justice_sentences", "columns": ["neighborhood", "count", "clinic_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO bi.bi_campaigns_delta SELECT healthcareid, singer_id, saleid FROM ancient_artifacts WHERE healthcareid > 144;\nEOF\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": ["healthcareid", "singer_id", "saleid"]}], "writes": [{"table": "bi.bi_campaigns_delta", "columns": ["healthcareid", "singer_id", "saleid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ma_inspections\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"reporters\")\n", "labels": {"reads": [{"table": "ma_inspections", "columns": null}], "writes": [{"table": "reporters", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workerbuildings\"\n", "labels": {"reads": [{"table": "workerbuildings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO us_military_personnel SELECT part_fault_id, license_number, workers FROM train WHERE part_fault_id > 367\"\n", "labels": {"reads": [{"table": "train", "columns": ["part_fault_id", "license_number", "workers"]}], "writes": [{"table": "us_military_personnel", "columns": ["part_fault_id", "license_number", "workers"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO water_distribution SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO artworks SELECT project_number, lastname FROM preferences WHERE project_number > 229\")\n", "labels": {"reads": [{"table": "preferences", "columns": ["project_number", "lastname"]}], "writes": [{"table": "artworks", "columns": ["project_number", "lastname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg.stg_inventory_hourly SELECT accreditation_level, sport FROM ods.ods_risk_score_delta WHERE accreditation_level > 221\"\n", "labels": {"reads": [{"table": "ods.ods_risk_score_delta", "columns": ["accreditation_level", "sport"]}], "writes": [{"table": "stg.stg_inventory_hourly", "columns": ["accreditation_level", "sport"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mart.member_point_df SELECT decision, commodity, billingcountry FROM bi.bi_events_df WHERE decision > 478;\nEOF\n", "labels": {"reads": [{"table": "bi.bi_events_df", "columns": ["decision", "commodity", "billingcountry"]}], "writes": [{"table": "mart.member_point_df", "columns": ["decision", "commodity", "billingcountry"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"workforcediversity\")\nsink_to_store(df, \"faculty\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "workforcediversity", "columns": null}], "writes": [{"table": "faculty", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO disaster_zones SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"timbersales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"infrastructurebudget\")\n", "labels": {"reads": [{"table": "timbersales", "columns": null}], "writes": [{"table": "infrastructurebudget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model inventory_di depends on crop_temperature\ndbt build --models inventory_di --vars '{\"src\":\"crop_temperature\"}'\n", "labels": {"reads": [{"table": "crop_temperature", "columns": null}], "writes": [{"table": "inventory_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT foreign, event FROM athletes\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"florida_conservation_initiatives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "athletes", "columns": ["foreign", "event"]}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd_risk_score_hourly --columns contract_start,vesselname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd_risk_score_hourly", "columns": ["contract_start", "vesselname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart_cart_item_di\", conn)\ndf.to_sql(\"dws.exposure\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart_cart_item_di", "columns": null}], "writes": [{"table": "dws.exposure", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"aus_wellbeing\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO time_dim SELECT tasktype, manager_name FROM menuitems WHERE tasktype > 345\")\n", "labels": {"reads": [{"table": "menuitems", "columns": ["tasktype", "manager_name"]}], "writes": [{"table": "time_dim", "columns": ["tasktype", "manager_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"complaints_breakdown\").toPandas()\ndf[[\"price\", \"bioprocess_name\"]].to_sql(\"seafoodsouthafricakenya\", engine, index=False)\n", "labels": {"reads": [{"table": "complaints_breakdown", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": ["price", "bioprocess_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table miningdepartment --columns deliveryid,accreditation_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "miningdepartment", "columns": ["deliveryid", "accreditation_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT crs_credit, countryid FROM companies_extended LIMIT 32\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO bi.bi_sessions_hourly SELECT lot_id, meal_id, fieldid FROM paper_data WHERE lot_id > 366\")\n", "labels": {"reads": [{"table": "companies_extended", "columns": ["crs_credit", "countryid"]}, {"table": "paper_data", "columns": ["lot_id", "meal_id", "fieldid"]}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": ["lot_id", "meal_id", "fieldid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM investor_activities\"\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT treasurer_vote, views FROM fair_trade_suppliers LIMIT 165\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO soccer_goals SELECT artifacttype, target_name, visitorid, game_count FROM marinespeciesobservations WHERE artifacttype > 234\")\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": ["treasurer_vote", "views"]}, {"table": "marinespeciesobservations", "columns": ["artifacttype", "target_name", "visitorid", "game_count"]}], "writes": [{"table": "soccer_goals", "columns": ["artifacttype", "target_name", "visitorid", "game_count"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_per_country\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM arctic_marine_species\"\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO police_emergencies (laborproductivity, common_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "police_emergencies", "columns": ["laborproductivity", "common_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO bi.bi_inventory_delta SELECT a.wildlife_type_id, b.contract_type FROM stg.stg_exposure_daily a JOIN mars_spacecraft b ON a.billing_state = b.billing_state\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.stg_exposure_daily", "columns": null}, {"table": "mars_spacecraft", "columns": null}], "writes": [{"table": "bi.bi_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"farmer_details\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw.dw_member_point_hourly\")\n", "labels": {"reads": [{"table": "farmer_details", "columns": null}], "writes": [{"table": "dw.dw_member_point_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table agri_innovations --columns billingcountry,community --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "agri_innovations", "columns": ["billingcountry", "community"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model course_authors_and_tutors depends on member\ndbt build --select course_authors_and_tutors --vars '{\"source_table\":\"member\"}'\n", "labels": {"reads": [{"table": "member", "columns": null}], "writes": [{"table": "course_authors_and_tutors", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.state > 270).all()\n# src table: fuel_consumption\nengine.execute(\"INSERT INTO film_category SELECT * FROM fuel_consumption\")\n", "labels": {"reads": [{"table": "fuel_consumption", "columns": null}], "writes": [{"table": "film_category", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO recycling_rates_state SELECT * FROM legacy\ncur.execute(\"SELECT transaction_type_code, cancel_date FROM dwd_sessions_hourly LIMIT 448\")\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": ["transaction_type_code", "cancel_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO festivals SELECT a.location_description, b.class_section FROM exoplanet_discoveries a JOIN airport b ON a.trip_city = b.trip_city\"\n", "labels": {"reads": [{"table": "exoplanet_discoveries", "columns": null}, {"table": "airport", "columns": null}], "writes": [{"table": "festivals", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO coral_reefs SELECT completed, f_id FROM ods.clicks_full WHERE completed > 216;\nEOF\n", "labels": {"reads": [{"table": "ods.clicks_full", "columns": ["completed", "f_id"]}], "writes": [{"table": "coral_reefs", "columns": ["completed", "f_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT satellite_name, spf_level FROM schools LIMIT 401;\nINSERT INTO coach SELECT planned_delivery_date, socially_responsible FROM timed_status_of_things WHERE planned_delivery_date > 306;\nSQL\n", "labels": {"reads": [{"table": "schools", "columns": ["satellite_name", "spf_level"]}, {"table": "timed_status_of_things", "columns": ["planned_delivery_date", "socially_responsible"]}], "writes": [{"table": "coach", "columns": ["planned_delivery_date", "socially_responsible"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_mammals\").toPandas()\ndf[[\"hireid\", \"assists\"]].to_sql(\"seafoodsouthafricakenya\", engine, index=False)\n", "labels": {"reads": [{"table": "marine_mammals", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": ["hireid", "assists"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO rainfall_data (feb, last_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "rainfall_data", "columns": ["feb", "last_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_initiatives\").toPandas()\ndf[[\"street_address\", \"left_office\"]].to_sql(\"manager_award\", engine, index=False)\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "manager_award", "columns": ["street_address", "left_office"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO student_tests_taken (researcher, donor_country) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "student_tests_taken", "columns": ["researcher", "donor_country"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model campaigns_daily depends on customer_size_diversity\ndbt run --select campaigns_daily --vars '{\"source_table\":\"customer_size_diversity\"}'\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": null}], "writes": [{"table": "campaigns_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table us_cities --target-dir /tmp/land\n", "labels": {"reads": [{"table": "us_cities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dish_type, measurement_date FROM department_publications\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"counties\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "department_publications", "columns": ["dish_type", "measurement_date"]}], "writes": [{"table": "counties", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"ods.campaigns_di\")\nsink_to_store(df, \"genetics_stats.research_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.campaigns_di", "columns": null}], "writes": [{"table": "genetics_stats.research_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.refunds_daily SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"foodsafetyrecords\").toPandas()\ndf[[\"facility_name\", \"sea\"]].to_sql(\"traditional_arts\", engine, index=False)\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": null}], "writes": [{"table": "traditional_arts", "columns": ["facility_name", "sea"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ref_attraction_types SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods_products_delta SELECT a.max_temperature_f, b.recruitername FROM maintenancerequests a JOIN dw.dw_coupon_use_daily b ON a.project_education = b.project_education\"\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": null}, {"table": "dw.dw_coupon_use_daily", "columns": null}], "writes": [{"table": "ods_products_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"procedures\")\npush_to_output(df, \"employeepromotions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "procedures", "columns": null}], "writes": [{"table": "employeepromotions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO store (call_id, water_usage) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "store", "columns": ["call_id", "water_usage"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT clinic_id, award FROM courses LIMIT 346\")\nimport logging\nspark.sql(\"INSERT INTO tryout SELECT billingcountry, authorder, medical_risk FROM volunteerprograms WHERE billingcountry > 205\")\n", "labels": {"reads": [{"table": "courses", "columns": ["clinic_id", "award"]}, {"table": "volunteerprograms", "columns": ["billingcountry", "authorder", "medical_risk"]}], "writes": [{"table": "tryout", "columns": ["billingcountry", "authorder", "medical_risk"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO wildlife SELECT chemical_id, document_type_code FROM tryout WHERE chemical_id > 362\"\n", "labels": {"reads": [{"table": "tryout", "columns": ["chemical_id", "document_type_code"]}], "writes": [{"table": "wildlife", "columns": ["chemical_id", "document_type_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO bi.bi_campaigns_daily SELECT a.creation, b.supply_volume FROM consumer_preference a JOIN casesbyyear b ON a.aid_id = b.aid_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}, {"table": "casesbyyear", "columns": null}], "writes": [{"table": "bi.bi_campaigns_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wellbeing_programs\").toPandas()\ndf[[\"personnel_id\", \"host_city_id\"]].to_sql(\"media_types\", engine, index=False)\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": null}], "writes": [{"table": "media_types", "columns": ["personnel_id", "host_city_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"creativeais\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"document_types\")\n", "labels": {"reads": [{"table": "creativeais", "columns": null}], "writes": [{"table": "document_types", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO pollution_initiatives SELECT cargoid, assessment_date FROM tweets WHERE cargoid > 83\"], check=True)\n", "labels": {"reads": [{"table": "tweets", "columns": ["cargoid", "assessment_date"]}], "writes": [{"table": "pollution_initiatives", "columns": ["cargoid", "assessment_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vocals (invoice_number, zip) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vocals", "columns": ["invoice_number", "zip"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"indigenouscommunities\")\nsrc.write.insertInto(\"all_programs\", overwrite=True)\n", "labels": {"reads": [{"table": "indigenouscommunities", "columns": null}], "writes": [{"table": "all_programs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT coalid, assists FROM donationhistory LIMIT 413\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO astronaut_missions SELECT trial_name, gdp FROM donation WHERE trial_name > 443\")\n", "labels": {"reads": [{"table": "donationhistory", "columns": ["coalid", "assists"]}, {"table": "donation", "columns": ["trial_name", "gdp"]}], "writes": [{"table": "astronaut_missions", "columns": ["trial_name", "gdp"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_payments_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_payments_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO locations_oceania SELECT a.business_size, b.mappingid FROM gamestats a JOIN fish_biomass b ON a.score = b.score\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "gamestats", "columns": null}, {"table": "fish_biomass", "columns": null}], "writes": [{"table": "locations_oceania", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT meter_100, framework_name FROM customer_payments\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"exhibition_record\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customer_payments", "columns": ["meter_100", "framework_name"]}], "writes": [{"table": "exhibition_record", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO incident_region SELECT * FROM legacy\ncur.execute(\"SELECT forest_type, ai_id FROM recyclers LIMIT 417\")\n", "labels": {"reads": [{"table": "recyclers", "columns": ["forest_type", "ai_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model arctic_research depends on socially_responsible_loans\ndbt build --select arctic_research --vars 'source: socially_responsible_loans'\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": null}], "writes": [{"table": "arctic_research", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nsql = \"INSERT INTO yoga SELECT a.problem_log_id, b.caseid FROM artifactanalysis a JOIN vehicle_registrations b ON a.region_code = b.region_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}, {"table": "vehicle_registrations", "columns": null}], "writes": [{"table": "yoga", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fair_wages\", conn)\ndf.to_sql(\"mart.clicks\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fair_wages", "columns": null}], "writes": [{"table": "mart.clicks", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT aircraft, policy_holder_id FROM program_budget LIMIT 479\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "program_budget", "columns": ["aircraft", "policy_holder_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.participant > 282).all()\n# src table: journal_committee\nengine.execute(\"INSERT INTO offender_demographics SELECT * FROM journal_committee\")\n", "labels": {"reads": [{"table": "journal_committee", "columns": null}], "writes": [{"table": "offender_demographics", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT stu_hrs, bats FROM parks LIMIT 177\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "parks", "columns": ["stu_hrs", "bats"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table category_revenue --columns policy_count,regulation --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "category_revenue", "columns": ["policy_count", "regulation"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO economic_diversification_argentina SELECT a.time_stamp, b.transaction_amount FROM az_drought_impact a JOIN open_pedagogy b ON a.frequency = b.frequency\"\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": null}, {"table": "open_pedagogy", "columns": null}], "writes": [{"table": "economic_diversification_argentina", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO vrusers SELECT a.routeid, b.engagement FROM expenses a JOIN nomination b ON a.price = b.price\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "expenses", "columns": null}, {"table": "nomination", "columns": null}], "writes": [{"table": "vrusers", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO community_programs SELECT ll_id, founder_veteran, hours_contributed, treatment_id FROM industrial_customers WHERE ll_id > 343\"\n", "labels": {"reads": [{"table": "industrial_customers", "columns": ["ll_id", "founder_veteran", "hours_contributed", "treatment_id"]}], "writes": [{"table": "community_programs", "columns": ["ll_id", "founder_veteran", "hours_contributed", "treatment_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"employee\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dws.dws_cart_item_daily\")\n", "labels": {"reads": [{"table": "employee", "columns": null}], "writes": [{"table": "dws.dws_cart_item_daily", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO dwd.dwd_payments_full SELECT a.maxoccupancy, b.winning_pilot FROM dws.dws_member_point_df a JOIN fans_merchandise_basketball b ON a.chemical = b.chemical\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": null}, {"table": "fans_merchandise_basketball", "columns": null}], "writes": [{"table": "dwd.dwd_payments_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"whale_sightings\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"levees\")\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}], "writes": [{"table": "levees", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO hospitals SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO county SELECT fare_id, resolution FROM mentalhealthprovider WHERE fare_id > 334\"\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": ["fare_id", "resolution"]}], "writes": [{"table": "county", "columns": ["fare_id", "resolution"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table stores_2 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stores_2", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO defense_spending_3 SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO recyclers SELECT currency_code, method_id, destinationid FROM industrial_customers WHERE currency_code > 234\"], check=True)\n", "labels": {"reads": [{"table": "industrial_customers", "columns": ["currency_code", "method_id", "destinationid"]}], "writes": [{"table": "recyclers", "columns": ["currency_code", "method_id", "destinationid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"well_production\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.dwd_orders_di\")\n", "labels": {"reads": [{"table": "well_production", "columns": null}], "writes": [{"table": "dwd.dwd_orders_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table settlements --columns revenue,investors --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "settlements", "columns": ["revenue", "investors"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model coupon_use_delta depends on mart.mart_campaigns_daily\ndbt run --select coupon_use_delta --vars '{\"src\":\"mart.mart_campaigns_daily\"}'\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": null}], "writes": [{"table": "coupon_use_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods_payments_delta (price_in_dollar, experienceid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods_payments_delta", "columns": ["price_in_dollar", "experienceid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sitename > 3).all()\n# src table: climate_projects\nengine.execute(\"INSERT INTO artsales SELECT * FROM climate_projects\")\n", "labels": {"reads": [{"table": "climate_projects", "columns": null}], "writes": [{"table": "artsales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO party_events SELECT donationdate, donationyear FROM military_technology_projects WHERE donationdate > 475\"], check=True)\n", "labels": {"reads": [{"table": "military_technology_projects", "columns": ["donationdate", "donationyear"]}], "writes": [{"table": "party_events", "columns": ["donationdate", "donationyear"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO rural.bus_trips (customer_id, animal) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "rural.bus_trips", "columns": ["customer_id", "animal"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO events (workername, engagement_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "events", "columns": ["workername", "engagement_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"adrprograms\").toPandas()\ndf[[\"songname\", \"strategy\"]].to_sql(\"satellite_deployment\", engine, index=False)\n", "labels": {"reads": [{"table": "adrprograms", "columns": null}], "writes": [{"table": "satellite_deployment", "columns": ["songname", "strategy"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO water_conservation_brazil (available_yn, farmname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "water_conservation_brazil", "columns": ["available_yn", "farmname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO rural_resources SELECT employeeid, volunteer_hours, pediatrician_id FROM storage_tech WHERE employeeid > 369\"\n", "labels": {"reads": [{"table": "storage_tech", "columns": ["employeeid", "volunteer_hours", "pediatrician_id"]}], "writes": [{"table": "rural_resources", "columns": ["employeeid", "volunteer_hours", "pediatrician_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM energy_consumption\", conn)\ndf.to_sql(\"stg.stg_products_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "energy_consumption", "columns": null}], "writes": [{"table": "stg.stg_products_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tech_volunteers (amount_settled, volume_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tech_volunteers", "columns": ["amount_settled", "volume_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT customerid, ota_id FROM makeup_products LIMIT 375\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO collective_bargaining SELECT launched_year, personnelid, trip_duration FROM caribbeansea WHERE launched_year > 461\")\n", "labels": {"reads": [{"table": "makeup_products", "columns": ["customerid", "ota_id"]}, {"table": "caribbeansea", "columns": ["launched_year", "personnelid", "trip_duration"]}], "writes": [{"table": "collective_bargaining", "columns": ["launched_year", "personnelid", "trip_duration"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"fossil_fuel_vehicles_japan\")\nupsert_to_sink(df, \"player_coach\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": null}], "writes": [{"table": "player_coach", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM stories\", conn)\ndf.to_sql(\"view_unit_status\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "stories", "columns": null}], "writes": [{"table": "view_unit_status", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT crop_id, developer FROM territory.human_rights_data LIMIT 328;\nINSERT INTO incidents_by_month SELECT document_structure_description, participatedinesports, hub_id, objectnumber FROM consumer WHERE document_structure_description > 183;\nSQL\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": ["crop_id", "developer"]}, {"table": "consumer", "columns": ["document_structure_description", "participatedinesports", "hub_id", "objectnumber"]}], "writes": [{"table": "incidents_by_month", "columns": ["document_structure_description", "participatedinesports", "hub_id", "objectnumber"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT competition, student_details FROM marketingbudget LIMIT 165\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["competition", "student_details"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climate_investments SELECT temporary_acting, analysis_date FROM dw.events_hourly WHERE temporary_acting > 357\"\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": ["temporary_acting", "analysis_date"]}], "writes": [{"table": "climate_investments", "columns": ["temporary_acting", "analysis_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO subway_stations_seoul SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT invoice_date, class_senator_vote FROM trainmaintenance LIMIT 472\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": ["invoice_date", "class_senator_vote"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO smartcitycosts SELECT access_count, sighting_date FROM ads.risk_score WHERE access_count > 250\")\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["access_count", "sighting_date"]}], "writes": [{"table": "smartcitycosts", "columns": ["access_count", "sighting_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT workout_type, shale_play FROM smartcities\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"drama_workshop_groups\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "smartcities", "columns": ["workout_type", "shale_play"]}], "writes": [{"table": "drama_workshop_groups", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO plots (clean_jerk, aid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "plots", "columns": ["clean_jerk", "aid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"on_call\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "on_call", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO catalog_contents_additional_attributes SELECT sponsor_name, vulnerability FROM bi_refunds_daily WHERE sponsor_name > 214\"\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["sponsor_name", "vulnerability"]}], "writes": [{"table": "catalog_contents_additional_attributes", "columns": ["sponsor_name", "vulnerability"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_member_point_df SELECT a.staff_name, b.exhibit_location FROM claims_processing_stages a JOIN ship b ON a.shale_play = b.shale_play\"\n", "labels": {"reads": [{"table": "claims_processing_stages", "columns": null}, {"table": "ship", "columns": null}], "writes": [{"table": "mart.mart_member_point_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO constructors SELECT * FROM legacy\ncur.execute(\"SELECT retailer, stream_id FROM carbon_emissions LIMIT 50\")\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": ["retailer", "stream_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO communication_scores SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO bustrips SELECT quality, paper_id, subscribe_date FROM students_lifelong_learning WHERE quality > 119\"\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": ["quality", "paper_id", "subscribe_date"]}], "writes": [{"table": "bustrips", "columns": ["quality", "paper_id", "subscribe_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model endowment depends on mailshot_campaigns\ndbt run -s endowment --vars 'source: mailshot_campaigns'\n", "labels": {"reads": [{"table": "mailshot_campaigns", "columns": null}], "writes": [{"table": "endowment", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO licenses (eid, sd_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "licenses", "columns": ["eid", "sd_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT energy_production, gametype FROM bi.bi_payments_full LIMIT 249\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "bi.bi_payments_full", "columns": ["energy_production", "gametype"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"astronaut_medical_3\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"collective_bargaining\")\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": null}], "writes": [{"table": "collective_bargaining", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO global_sales_2022 SELECT cloud_cover, vehicle_type FROM dwd.dwd_member_point_full WHERE cloud_cover > 485\"\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_full", "columns": ["cloud_cover", "vehicle_type"]}], "writes": [{"table": "global_sales_2022", "columns": ["cloud_cover", "vehicle_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fault_log_parts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"disaster_zones\")\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": null}], "writes": [{"table": "disaster_zones", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM rating\"\n", "labels": {"reads": [{"table": "rating", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_products_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ods_exposure_delta\")\n", "labels": {"reads": [{"table": "stg.stg_products_full", "columns": null}], "writes": [{"table": "ods_exposure_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.watertemp > 322).all()\n# src table: financial_transactions\nengine.execute(\"INSERT INTO gymc_members SELECT * FROM financial_transactions\")\n", "labels": {"reads": [{"table": "financial_transactions", "columns": null}], "writes": [{"table": "gymc_members", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridgerainfall\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bridgerainfall", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO legal_technology_funding SELECT song_name, bank_id FROM higher_ed.students WHERE song_name > 168\"], check=True)\n", "labels": {"reads": [{"table": "higher_ed.students", "columns": ["song_name", "bank_id"]}], "writes": [{"table": "legal_technology_funding", "columns": ["song_name", "bank_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO user_profiles SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"news_stories\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"artistsales\")\n", "labels": {"reads": [{"table": "news_stories", "columns": null}], "writes": [{"table": "artistsales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"scientists\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bike_share\")\n", "labels": {"reads": [{"table": "scientists", "columns": null}], "writes": [{"table": "bike_share", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO chip_model SELECT * FROM legacy\ncur.execute(\"SELECT transaction_type_code, incident_id FROM machines LIMIT 42\")\n", "labels": {"reads": [{"table": "machines", "columns": ["transaction_type_code", "incident_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO entrepreneur SELECT consultations, complaintid FROM customer_payments WHERE consultations > 418\"], check=True)\n", "labels": {"reads": [{"table": "customer_payments", "columns": ["consultations", "complaintid"]}], "writes": [{"table": "entrepreneur", "columns": ["consultations", "complaintid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"product_sales\")\npush_to_target(df, \"droughthistory\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "product_sales", "columns": null}], "writes": [{"table": "droughthistory", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO waste_types SELECT a.sales_amount, b.transaction_id FROM public.forest_stats a JOIN erc20_transactions b ON a.supplier = b.supplier\"\n", "labels": {"reads": [{"table": "public.forest_stats", "columns": null}, {"table": "erc20_transactions", "columns": null}], "writes": [{"table": "waste_types", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table safety_research --target-dir /tmp/land\n", "labels": {"reads": [{"table": "safety_research", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO miningwaterusage (vegan, artifact_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "miningwaterusage", "columns": ["vegan", "artifact_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"precipitation_data\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"savings_programs\")\n", "labels": {"reads": [{"table": "precipitation_data", "columns": null}], "writes": [{"table": "savings_programs", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods.ods_sessions_df SELECT don_id, rate FROM player WHERE don_id > 277\"\n", "labels": {"reads": [{"table": "player", "columns": ["don_id", "rate"]}], "writes": [{"table": "ods.ods_sessions_df", "columns": ["don_id", "rate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ads.member_point --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads.member_point", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO usdaviolations SELECT workshop_id, login_name FROM dws.dws_member_point_df WHERE workshop_id > 308\")\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": ["workshop_id", "login_name"]}], "writes": [{"table": "usdaviolations", "columns": ["workshop_id", "login_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attribute_data_type, mental_health_status FROM prepaid_mobile\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"shariah_compliant_loans\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "prepaid_mobile", "columns": ["attribute_data_type", "mental_health_status"]}], "writes": [{"table": "shariah_compliant_loans", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO building_permits SELECT assigned_to_staff_id, product_color, energy_efficiency_kwh_m2_year FROM militaryoperations WHERE assigned_to_staff_id > 273\"\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["assigned_to_staff_id", "product_color", "energy_efficiency_kwh_m2_year"]}], "writes": [{"table": "building_permits", "columns": ["assigned_to_staff_id", "product_color", "energy_efficiency_kwh_m2_year"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model india_solar_power depends on africa_projects\ndbt build --models india_solar_power --vars '{\"source_table\":\"africa_projects\"}'\n", "labels": {"reads": [{"table": "africa_projects", "columns": null}], "writes": [{"table": "india_solar_power", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT hometown, tier FROM gender\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"unionmembers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "gender", "columns": ["hometown", "tier"]}], "writes": [{"table": "unionmembers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"habitats\")\npush_to_store(df, \"visits\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "habitats", "columns": null}], "writes": [{"table": "visits", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT unavailable, shipping_mode FROM book LIMIT 382\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "book", "columns": ["unavailable", "shipping_mode"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"waste_data\")\nsrc.write.insertInto(\"stg.stg_events_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "waste_data", "columns": null}], "writes": [{"table": "stg.stg_events_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.trench_name > 417).all()\n# src table: livestock\nengine.execute(\"INSERT INTO recyclingprogram SELECT * FROM livestock\")\n", "labels": {"reads": [{"table": "livestock", "columns": null}], "writes": [{"table": "recyclingprogram", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"mart.inventory_hourly\")\nsave_to_warehouse(df, \"food_justice_orgs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": null}], "writes": [{"table": "food_justice_orgs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table crops_table --columns energy_generated,pollutant_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "crops_table", "columns": ["energy_generated", "pollutant_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT market_rate, founders FROM technology_access\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ndf.to_sql(\"green_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "technology_access", "columns": ["market_rate", "founders"]}], "writes": [{"table": "green_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.deaths > 336).all()\n# src table: user_reactions\nengine.execute(\"INSERT INTO hospitallocations SELECT * FROM user_reactions\")\n", "labels": {"reads": [{"table": "user_reactions", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO continents SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donors_region\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"climateresearch\")\n", "labels": {"reads": [{"table": "donors_region", "columns": null}], "writes": [{"table": "climateresearch", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_sessions\").toPandas()\ndf[[\"region_code\", \"gas_production_2020\"]].to_sql(\"totalenergyproduction\", engine, index=False)\n", "labels": {"reads": [{"table": "ods_sessions", "columns": null}], "writes": [{"table": "totalenergyproduction", "columns": ["region_code", "gas_production_2020"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO circular_economy_companies (business_size, ll_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "circular_economy_companies", "columns": ["business_size", "ll_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"euroavev\").toPandas()\ndf[[\"operationdate\", \"analysis_date\"]].to_sql(\"yearly_production\", engine, index=False)\n", "labels": {"reads": [{"table": "euroavev", "columns": null}], "writes": [{"table": "yearly_production", "columns": ["operationdate", "analysis_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_clinics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"item_prices\")\n", "labels": {"reads": [{"table": "rural_clinics", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO onlineengagement SELECT rural_area, client_id FROM ads_coupon_use_full WHERE rural_area > 146;\nEOF\n", "labels": {"reads": [{"table": "ads_coupon_use_full", "columns": ["rural_area", "client_id"]}], "writes": [{"table": "onlineengagement", "columns": ["rural_area", "client_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_sessions_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"canada_cosmetics_preferences\")\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": null}], "writes": [{"table": "canada_cosmetics_preferences", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO unesco_intangible_heritage SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT acidity, friend FROM rural_projects\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"mart.mart_refunds_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "rural_projects", "columns": ["acidity", "friend"]}], "writes": [{"table": "mart.mart_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"race_ethnicity\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"manufacturermaterials\")\n", "labels": {"reads": [{"table": "race_ethnicity", "columns": null}], "writes": [{"table": "manufacturermaterials", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ads.inventory_di SELECT * FROM legacy\ncur.execute(\"SELECT scoreid, class_room FROM cultivators LIMIT 346\")\n", "labels": {"reads": [{"table": "cultivators", "columns": ["scoreid", "class_room"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO energy_production SELECT brandname, total_investment, emp_lname FROM apartment_facilities WHERE brandname > 441\"\n", "labels": {"reads": [{"table": "apartment_facilities", "columns": ["brandname", "total_investment", "emp_lname"]}], "writes": [{"table": "energy_production", "columns": ["brandname", "total_investment", "emp_lname"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO flights (hashtags, building_description) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "flights", "columns": ["hashtags", "building_description"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sitem\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artwork_styles\")\n", "labels": {"reads": [{"table": "sitem", "columns": null}], "writes": [{"table": "artwork_styles", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"record\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"genetics.crispr\")\n", "labels": {"reads": [{"table": "record", "columns": null}], "writes": [{"table": "genetics.crispr", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT attendance, fair_trade FROM council_tax\", engine)\nimport logging\ndf.to_sql(\"college\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "council_tax", "columns": ["attendance", "fair_trade"]}], "writes": [{"table": "college", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM safety_research\"\n", "labels": {"reads": [{"table": "safety_research", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO police_emergencies SELECT material_name, resource_name FROM safetytests WHERE material_name > 459\"\n", "labels": {"reads": [{"table": "safetytests", "columns": ["material_name", "resource_name"]}], "writes": [{"table": "police_emergencies", "columns": ["material_name", "resource_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO levees SELECT gender_mf, meal_name, activity_id, condition_id FROM furniture WHERE gender_mf > 204\"\n", "labels": {"reads": [{"table": "furniture", "columns": ["gender_mf", "meal_name", "activity_id", "condition_id"]}], "writes": [{"table": "levees", "columns": ["gender_mf", "meal_name", "activity_id", "condition_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.dw_payments_full\")\nsrc.write.insertInto(\"project_timelines\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.dw_payments_full", "columns": null}], "writes": [{"table": "project_timelines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT stay, secretary_vote FROM cargo_tracking LIMIT 415\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO agroecology_practices SELECT trade, fair_labor, screening FROM arctic_weather WHERE trade > 399\")\n", "labels": {"reads": [{"table": "cargo_tracking", "columns": ["stay", "secretary_vote"]}, {"table": "arctic_weather", "columns": ["trade", "fair_labor", "screening"]}], "writes": [{"table": "agroecology_practices", "columns": ["trade", "fair_labor", "screening"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"reservoirs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "reservoirs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"projecttimelinebybudget\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mission\")\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": null}], "writes": [{"table": "mission", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT product_description, card_id FROM textile_sourcing\", engine)\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"classroom\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "textile_sourcing", "columns": ["product_description", "card_id"]}], "writes": [{"table": "classroom", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM healthcareaccess\"\n", "labels": {"reads": [{"table": "healthcareaccess", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO league_x SELECT investment, successful_cb, position FROM threatintelligence WHERE investment > 428\"\n", "labels": {"reads": [{"table": "threatintelligence", "columns": ["investment", "successful_cb", "position"]}], "writes": [{"table": "league_x", "columns": ["investment", "successful_cb", "position"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO urban_farms SELECT * FROM legacy\ncur.execute(\"SELECT cname, class_section FROM ods.inventory_df LIMIT 184\")\n", "labels": {"reads": [{"table": "ods.inventory_df", "columns": ["cname", "class_section"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO networkdevices SELECT share_in_percent, trip_type, workshop_id FROM program_funding_2 WHERE share_in_percent > 65\")\n", "labels": {"reads": [{"table": "program_funding_2", "columns": ["share_in_percent", "trip_type", "workshop_id"]}], "writes": [{"table": "networkdevices", "columns": ["share_in_percent", "trip_type", "workshop_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT competition_type, union_member_id FROM dispensaries LIMIT 243\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "dispensaries", "columns": ["competition_type", "union_member_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO shark_biomass SELECT clean_date, attraction_name FROM business_rates WHERE clean_date > 460\"\n", "labels": {"reads": [{"table": "business_rates", "columns": ["clean_date", "attraction_name"]}], "writes": [{"table": "shark_biomass", "columns": ["clean_date", "attraction_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO specieswatertemp SELECT a.age, b.mouse_id FROM community_health_centers a JOIN crops b ON a.wellbeing_score = b.wellbeing_score\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "community_health_centers", "columns": null}, {"table": "crops", "columns": null}], "writes": [{"table": "specieswatertemp", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO checking SELECT household_size, coupon_id, claimtype, conferencename FROM customer_address_history WHERE household_size > 351\"\n", "labels": {"reads": [{"table": "customer_address_history", "columns": ["household_size", "coupon_id", "claimtype", "conferencename"]}], "writes": [{"table": "checking", "columns": ["household_size", "coupon_id", "claimtype", "conferencename"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model state_contracts depends on spaceradar\ndbt run --models state_contracts --vars 'source: spaceradar'\n", "labels": {"reads": [{"table": "spaceradar", "columns": null}], "writes": [{"table": "state_contracts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT floor_exercise_points, worker_id FROM attendee_demographics LIMIT 447\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": ["floor_exercise_points", "worker_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT account_type, wins FROM indigenouscommunities LIMIT 224\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO judges SELECT playergameid, lot_details, vendor, restaurant_id FROM visits_restaurant WHERE playergameid > 445\")\n", "labels": {"reads": [{"table": "indigenouscommunities", "columns": ["account_type", "wins"]}, {"table": "visits_restaurant", "columns": ["playergameid", "lot_details", "vendor", "restaurant_id"]}], "writes": [{"table": "judges", "columns": ["playergameid", "lot_details", "vendor", "restaurant_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO pollution_control_initiatives SELECT a.crs_code, b.scientific_name FROM ticket_sales a JOIN stg_payments_hourly b ON a.founders = b.founders\"\n", "labels": {"reads": [{"table": "ticket_sales", "columns": null}, {"table": "stg_payments_hourly", "columns": null}], "writes": [{"table": "pollution_control_initiatives", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO maintenance SELECT * FROM legacy\ncur.execute(\"SELECT attribute_data_type, forest_id FROM open_data_initiatives LIMIT 268\")\n", "labels": {"reads": [{"table": "open_data_initiatives", "columns": ["attribute_data_type", "forest_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_spent > 353).all()\n# src table: highways\nengine.execute(\"INSERT INTO settlements SELECT * FROM highways\")\n", "labels": {"reads": [{"table": "highways", "columns": null}], "writes": [{"table": "settlements", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO routes SELECT * FROM legacy\ncur.execute(\"SELECT policy_type, attendance_date FROM biomes LIMIT 316\")\n", "labels": {"reads": [{"table": "biomes", "columns": ["policy_type", "attendance_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT movie_id, duration_ms FROM soil_moisture LIMIT 215\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bi.risk_score_df SELECT province, species_id, flight_number FROM attorney_billing WHERE province > 200\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["movie_id", "duration_ms"]}, {"table": "attorney_billing", "columns": ["province", "species_id", "flight_number"]}], "writes": [{"table": "bi.risk_score_df", "columns": ["province", "species_id", "flight_number"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.clicks_di\").toPandas()\ndf[[\"bats\", \"production\"]].to_sql(\"producesupplier\", engine, index=False)\n", "labels": {"reads": [{"table": "dw.clicks_di", "columns": null}], "writes": [{"table": "producesupplier", "columns": ["bats", "production"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ref_shipping_agents SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exit_strategy\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads_refunds_full\")\n", "labels": {"reads": [{"table": "exit_strategy", "columns": null}], "writes": [{"table": "ads_refunds_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO production_sites SELECT a.coupon_id, b.subscriber_id FROM decentralized_apps a JOIN climate_communication b ON a.zone_name = b.zone_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "decentralized_apps", "columns": null}, {"table": "climate_communication", "columns": null}], "writes": [{"table": "production_sites", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT attendeename, home_city FROM election LIMIT 47\")\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO attack_outcomes SELECT ram_mib, incident_type FROM rural.bus_trips WHERE ram_mib > 85\")\n", "labels": {"reads": [{"table": "election", "columns": ["attendeename", "home_city"]}, {"table": "rural.bus_trips", "columns": ["ram_mib", "incident_type"]}], "writes": [{"table": "attack_outcomes", "columns": ["ram_mib", "incident_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO ocean_floor_mapping SELECT pipeline_name, customer_id, course_completion FROM dws.events WHERE pipeline_name > 297\"\n", "labels": {"reads": [{"table": "dws.events", "columns": ["pipeline_name", "customer_id", "course_completion"]}], "writes": [{"table": "ocean_floor_mapping", "columns": ["pipeline_name", "customer_id", "course_completion"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dws.dws_users_hourly SELECT trial_success_rate, underrepresented_community, color_code, supplier_company_id FROM dws.risk_score_daily WHERE trial_success_rate > 467\"\n", "labels": {"reads": [{"table": "dws.risk_score_daily", "columns": ["trial_success_rate", "underrepresented_community", "color_code", "supplier_company_id"]}], "writes": [{"table": "dws.dws_users_hourly", "columns": ["trial_success_rate", "underrepresented_community", "color_code", "supplier_company_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO exhibition_record SELECT * FROM legacy\ncur.execute(\"SELECT worker, appointmentid FROM energy_prices LIMIT 128\")\n", "labels": {"reads": [{"table": "energy_prices", "columns": ["worker", "appointmentid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO ads.ads_cart_item_hourly SELECT a.assessmentname, b.threats FROM artworks a JOIN program_funding_2 b ON a.project_details = b.project_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artworks", "columns": null}, {"table": "program_funding_2", "columns": null}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"drought_impact\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO workplaces SELECT a.sale_revenue, b.allocation_date FROM safetytestingcounts a JOIN marine_life_data b ON a.delivery_time = b.delivery_time\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "safetytestingcounts", "columns": null}, {"table": "marine_life_data", "columns": null}], "writes": [{"table": "workplaces", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO canada_tech SELECT dissolved_oxygen, coalid, customer_country, submission_id FROM continents WHERE dissolved_oxygen > 218\"\n", "labels": {"reads": [{"table": "continents", "columns": ["dissolved_oxygen", "coalid", "customer_country", "submission_id"]}], "writes": [{"table": "canada_tech", "columns": ["dissolved_oxygen", "coalid", "customer_country", "submission_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model reservations depends on player\ndbt build --models reservations --vars '{\"src\":\"player\"}'\n", "labels": {"reads": [{"table": "player", "columns": null}], "writes": [{"table": "reservations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO open_data_initiatives (min_dew_point_f, salesperson_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "open_data_initiatives", "columns": ["min_dew_point_f", "salesperson_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_pallets, call_date FROM mobile_usage LIMIT 187\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO lessons SELECT person_id, vaccine_name, quality_rank FROM ingredientsvegancrueltyfree WHERE person_id > 100\")\n", "labels": {"reads": [{"table": "mobile_usage", "columns": ["num_pallets", "call_date"]}, {"table": "ingredientsvegancrueltyfree", "columns": ["person_id", "vaccine_name", "quality_rank"]}], "writes": [{"table": "lessons", "columns": ["person_id", "vaccine_name", "quality_rank"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model defenseprojects depends on veterans\ndbt run -s defenseprojects --vars '{\"src\":\"veterans\"}'\n", "labels": {"reads": [{"table": "veterans", "columns": null}], "writes": [{"table": "defenseprojects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM animal_populations\"\n", "labels": {"reads": [{"table": "animal_populations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"ucl_top10\")\nexport_to_sink(df, \"companies_extended\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ucl_top10", "columns": null}], "writes": [{"table": "companies_extended", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO mart.vendors_full SELECT school_colors, athlete, co2_emission FROM chemical_concentration WHERE school_colors > 134\")\n", "labels": {"reads": [{"table": "chemical_concentration", "columns": ["school_colors", "athlete", "co2_emission"]}], "writes": [{"table": "mart.vendors_full", "columns": ["school_colors", "athlete", "co2_emission"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM water_usage\"\n", "labels": {"reads": [{"table": "water_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM donationprograms\"\n", "labels": {"reads": [{"table": "donationprograms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recycled_polyester\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"catalog_contents_additional_attributes\")\n", "labels": {"reads": [{"table": "recycled_polyester", "columns": null}], "writes": [{"table": "catalog_contents_additional_attributes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table restaurant_type --columns service_name,monthlyactiveusers --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "restaurant_type", "columns": ["service_name", "monthlyactiveusers"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.council_id > 454).all()\n# src table: city.community_policing\nengine.execute(\"INSERT INTO artprograms SELECT * FROM city.community_policing\")\n", "labels": {"reads": [{"table": "city.community_policing", "columns": null}], "writes": [{"table": "artprograms", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mining.company SELECT a.vendor_state, b.built_year FROM animal_population_status a JOIN culturalcompetencytraining b ON a.savings = b.savings\"\n", "labels": {"reads": [{"table": "animal_population_status", "columns": null}, {"table": "culturalcompetencytraining", "columns": null}], "writes": [{"table": "mining.company", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"textile_suppliers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"shariah_compliant_products\")\n", "labels": {"reads": [{"table": "textile_suppliers", "columns": null}], "writes": [{"table": "shariah_compliant_products", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table mining_operation --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mining_operation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model green_buildings depends on donations\ndbt run --models green_buildings --vars 'source: donations'\n", "labels": {"reads": [{"table": "donations", "columns": null}], "writes": [{"table": "green_buildings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stateinfrastructure\"\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO menu_engineering SELECT therapy_id, song_name, event FROM calibration_data2 WHERE therapy_id > 364\"\n", "labels": {"reads": [{"table": "calibration_data2", "columns": ["therapy_id", "song_name", "event"]}], "writes": [{"table": "menu_engineering", "columns": ["therapy_id", "song_name", "event"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO gymnast SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model research_staff depends on school\ndbt run --models research_staff --vars '{\"source_table\":\"school\"}'\n", "labels": {"reads": [{"table": "school", "columns": null}], "writes": [{"table": "research_staff", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO military_personnel_africa (city_name, service_type_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "military_personnel_africa", "columns": ["city_name", "service_type_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.inventory_daily SELECT zip, organization FROM route_fares WHERE zip > 53\"\n", "labels": {"reads": [{"table": "route_fares", "columns": ["zip", "organization"]}], "writes": [{"table": "bi.inventory_daily", "columns": ["zip", "organization"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO busmaintenance SELECT education_id, store_email_address, framework_id FROM pollutionincidents WHERE education_id > 358\")\n", "labels": {"reads": [{"table": "pollutionincidents", "columns": ["education_id", "store_email_address", "framework_id"]}], "writes": [{"table": "busmaintenance", "columns": ["education_id", "store_email_address", "framework_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT tank, brand_id FROM roles LIMIT 426\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "roles", "columns": ["tank", "brand_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tree_habitat_associations SELECT * FROM legacy\ncur.execute(\"SELECT vessel_name, operating_system FROM baseball_teams LIMIT 496\")\n", "labels": {"reads": [{"table": "baseball_teams", "columns": ["vessel_name", "operating_system"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"imagery_archive\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"community_programs\")\n", "labels": {"reads": [{"table": "imagery_archive", "columns": null}], "writes": [{"table": "community_programs", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"docking\")\nsrc.write.insertInto(\"product_details\", overwrite=True)\n", "labels": {"reads": [{"table": "docking", "columns": null}], "writes": [{"table": "product_details", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recycling_stats\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "recycling_stats", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_campaigns\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stg.stg_campaigns", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cust_id > 54).all()\n# src table: weather_record\nengine.execute(\"INSERT INTO shared_ebikes SELECT * FROM weather_record\")\n", "labels": {"reads": [{"table": "weather_record", "columns": null}], "writes": [{"table": "shared_ebikes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO product_catalog SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model humanitarianassistanceoperations depends on regions\ndbt run -s humanitarianassistanceoperations --vars '{\"source_table\":\"regions\"}'\n", "labels": {"reads": [{"table": "regions", "columns": null}], "writes": [{"table": "humanitarianassistanceoperations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO genetic_research SELECT * FROM legacy\ncur.execute(\"SELECT access_date, asset_type FROM autonomousdriving LIMIT 450\")\n", "labels": {"reads": [{"table": "autonomousdriving", "columns": ["access_date", "asset_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO operation SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fleet_management\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.coupon_use_daily\")\n", "labels": {"reads": [{"table": "fleet_management", "columns": null}], "writes": [{"table": "dwd.coupon_use_daily", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT port_id, event_type_id FROM innovation_trends LIMIT 274;\nINSERT INTO storage SELECT complaint_id, license_type, concert_id, decision FROM genetics_stats.research_projects WHERE complaint_id > 231;\nSQL\n", "labels": {"reads": [{"table": "innovation_trends", "columns": ["port_id", "event_type_id"]}, {"table": "genetics_stats.research_projects", "columns": ["complaint_id", "license_type", "concert_id", "decision"]}], "writes": [{"table": "storage", "columns": ["complaint_id", "license_type", "concert_id", "decision"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table donorprograms --columns trade,ota_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "donorprograms", "columns": ["trade", "ota_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bookings SELECT sustainable_practice, trip_duration FROM files WHERE sustainable_practice > 315\"\n", "labels": {"reads": [{"table": "files", "columns": ["sustainable_practice", "trip_duration"]}], "writes": [{"table": "bookings", "columns": ["sustainable_practice", "trip_duration"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart_events_full SELECT project_education, label_id, specialty, prod_id FROM dw_payments WHERE project_education > 389\"\n", "labels": {"reads": [{"table": "dw_payments", "columns": ["project_education", "label_id", "specialty", "prod_id"]}], "writes": [{"table": "mart_events_full", "columns": ["project_education", "label_id", "specialty", "prod_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO carbon_prices_3 SELECT document_status_description, instructor, performance_id, document_structure_code FROM laborstatistics WHERE document_status_description > 246\"], check=True)\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["document_status_description", "instructor", "performance_id", "document_structure_code"]}], "writes": [{"table": "carbon_prices_3", "columns": ["document_status_description", "instructor", "performance_id", "document_structure_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"climate_monitoring_stations\")\npush_to_output(df, \"item\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": null}], "writes": [{"table": "item", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO customer SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT level, destroyed_by_employee_id FROM dwd.campaigns LIMIT 58\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "dwd.campaigns", "columns": ["level", "destroyed_by_employee_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"satellites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"smart_contracts\")\n", "labels": {"reads": [{"table": "satellites", "columns": null}], "writes": [{"table": "smart_contracts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT trip_start_time, bname FROM packages LIMIT 410;\nINSERT INTO research SELECT founder_country, mealid FROM tourismproviders WHERE founder_country > 300;\nSQL\n", "labels": {"reads": [{"table": "packages", "columns": ["trip_start_time", "bname"]}, {"table": "tourismproviders", "columns": ["founder_country", "mealid"]}], "writes": [{"table": "research", "columns": ["founder_country", "mealid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT funding_source_type, stu_gpa FROM cybersecurity_vulnerabilities\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"stg.campaigns_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cybersecurity_vulnerabilities", "columns": ["funding_source_type", "stu_gpa"]}], "writes": [{"table": "stg.campaigns_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO police_officers_tx SELECT a.project_name, b.problem_description FROM safety_research a JOIN assets b ON a.popularity = b.popularity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "safety_research", "columns": null}, {"table": "assets", "columns": null}], "writes": [{"table": "police_officers_tx", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"benefits_overpayments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"communitydevelopment\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"higher_ed.students\")\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": null}], "writes": [{"table": "higher_ed.students", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT track_id, province_id FROM city_properties LIMIT 131;\nINSERT INTO levees SELECT professionalid, premise_id, purchase_transaction_id FROM mart.mart_users_df WHERE professionalid > 222;\nSQL\n", "labels": {"reads": [{"table": "city_properties", "columns": ["track_id", "province_id"]}, {"table": "mart.mart_users_df", "columns": ["professionalid", "premise_id", "purchase_transaction_id"]}], "writes": [{"table": "levees", "columns": ["professionalid", "premise_id", "purchase_transaction_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT bikes_available, innovation FROM fireincidents LIMIT 46\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "fireincidents", "columns": ["bikes_available", "innovation"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT attorney, highscore FROM insurancetype LIMIT 343;\nINSERT INTO player_sessions SELECT hoursspent, resolution, student_id FROM zip_codes WHERE hoursspent > 145;\nSQL\n", "labels": {"reads": [{"table": "insurancetype", "columns": ["attorney", "highscore"]}, {"table": "zip_codes", "columns": ["hoursspent", "resolution", "student_id"]}], "writes": [{"table": "player_sessions", "columns": ["hoursspent", "resolution", "student_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donationsbycause\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"video_content\")\n", "labels": {"reads": [{"table": "donationsbycause", "columns": null}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO regulatoryframeworksbycountry SELECT tech_id, memberid FROM constructorstandings WHERE tech_id > 123\"], check=True)\n", "labels": {"reads": [{"table": "constructorstandings", "columns": ["tech_id", "memberid"]}], "writes": [{"table": "regulatoryframeworksbycountry", "columns": ["tech_id", "memberid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT graphics_mode, year_built FROM cosmetic_sales LIMIT 251;\nINSERT INTO military_expenditure SELECT pipeline_name, brand_mentioned FROM mobile_usage WHERE pipeline_name > 376;\nSQL\n", "labels": {"reads": [{"table": "cosmetic_sales", "columns": ["graphics_mode", "year_built"]}, {"table": "mobile_usage", "columns": ["pipeline_name", "brand_mentioned"]}], "writes": [{"table": "military_expenditure", "columns": ["pipeline_name", "brand_mentioned"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM satellitedata\", conn)\ndf.to_sql(\"postseason\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "satellitedata", "columns": null}], "writes": [{"table": "postseason", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table attack_outcomes --columns visitorid,publication_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "attack_outcomes", "columns": ["visitorid", "publication_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO staff SELECT a.categoryid, b.room FROM leo_missions a JOIN organization b ON a.followers = b.followers\"\n", "labels": {"reads": [{"table": "leo_missions", "columns": null}, {"table": "organization", "columns": null}], "writes": [{"table": "staff", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO race_ethnicity SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO heritagesites SELECT a.num_attendees, b.lipstick_id FROM marine_life_populations a JOIN artcollection b ON a.pilot_name = b.pilot_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": null}, {"table": "artcollection", "columns": null}], "writes": [{"table": "heritagesites", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO labor_practices SELECT updatedate, mappinglength FROM conservation_programs WHERE updatedate > 255\")\n", "labels": {"reads": [{"table": "conservation_programs", "columns": ["updatedate", "mappinglength"]}], "writes": [{"table": "labor_practices", "columns": ["updatedate", "mappinglength"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO dw.shipments_df SELECT host_city, skill_description, tree_type_id FROM fan_purchases WHERE host_city > 470\"\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["host_city", "skill_description", "tree_type_id"]}], "writes": [{"table": "dw.shipments_df", "columns": ["host_city", "skill_description", "tree_type_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.brand > 362).all()\n# src table: dws.events\nengine.execute(\"INSERT INTO stg.device_log_df SELECT * FROM dws.events\")\n", "labels": {"reads": [{"table": "dws.events", "columns": null}], "writes": [{"table": "stg.device_log_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"urban_transportation\")\nsrc.write.insertInto(\"claims_processing_stages\", overwrite=True)\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}], "writes": [{"table": "claims_processing_stages", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artist SELECT approach, resident_id, grade FROM tech_accessibility_funding WHERE approach > 246\"\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": ["approach", "resident_id", "grade"]}], "writes": [{"table": "artist", "columns": ["approach", "resident_id", "grade"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table dwd.events_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.events_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"victims\").toPandas()\ndf[[\"visitdate\", \"waste_generation\"]].to_sql(\"stg.stg_coupon_use_di\", engine, index=False)\n", "labels": {"reads": [{"table": "victims", "columns": null}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": ["visitdate", "waste_generation"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table satellite_missions_large --target-dir /tmp/land\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT devices, closuredate FROM billstatus LIMIT 76;\nINSERT INTO team_franchise SELECT total_distance, monthly_rental, winning_aircraft FROM immunization WHERE total_distance > 57;\nSQL\n", "labels": {"reads": [{"table": "billstatus", "columns": ["devices", "closuredate"]}, {"table": "immunization", "columns": ["total_distance", "monthly_rental", "winning_aircraft"]}], "writes": [{"table": "team_franchise", "columns": ["total_distance", "monthly_rental", "winning_aircraft"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"plots\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "plots", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO shariah_compliant_products (courses, causeid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "shariah_compliant_products", "columns": ["courses", "causeid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mine_workforce\", conn)\ndf.to_sql(\"mental_health_clinics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mine_workforce", "columns": null}], "writes": [{"table": "mental_health_clinics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO innovation_projects (users_engaged, plant_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "innovation_projects", "columns": ["users_engaged", "plant_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM spacecraft_components\"\n", "labels": {"reads": [{"table": "spacecraft_components", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO user_stats SELECT amount_waste, fleet_series FROM state_energy WHERE amount_waste > 450\"], check=True)\n", "labels": {"reads": [{"table": "state_energy", "columns": ["amount_waste", "fleet_series"]}], "writes": [{"table": "user_stats", "columns": ["amount_waste", "fleet_series"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO city_waste_generation SELECT num_solo_exhibitions, stat_id, donor_program FROM bi_shipments_daily WHERE num_solo_exhibitions > 74\")\n", "labels": {"reads": [{"table": "bi_shipments_daily", "columns": ["num_solo_exhibitions", "stat_id", "donor_program"]}], "writes": [{"table": "city_waste_generation", "columns": ["num_solo_exhibitions", "stat_id", "donor_program"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.payments_daily SELECT ratingdate, vendor, opname, account_number FROM tracks WHERE ratingdate > 158\"], check=True)\n", "labels": {"reads": [{"table": "tracks", "columns": ["ratingdate", "vendor", "opname", "account_number"]}], "writes": [{"table": "bi.payments_daily", "columns": ["ratingdate", "vendor", "opname", "account_number"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO suppliersfairlabor SELECT investmentid, clientid, tour_type, next_entry_id FROM residents_services WHERE investmentid > 49\"\n", "labels": {"reads": [{"table": "residents_services", "columns": ["investmentid", "clientid", "tour_type", "next_entry_id"]}], "writes": [{"table": "suppliersfairlabor", "columns": ["investmentid", "clientid", "tour_type", "next_entry_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO renewable_power SELECT is_safe, character, budget_allocation FROM companies WHERE is_safe > 447\"\n", "labels": {"reads": [{"table": "companies", "columns": ["is_safe", "character", "budget_allocation"]}], "writes": [{"table": "renewable_power", "columns": ["is_safe", "character", "budget_allocation"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainability_metrics SELECT * FROM legacy\ncur.execute(\"SELECT initiative_region, resource_id FROM hydro_power LIMIT 404\")\n", "labels": {"reads": [{"table": "hydro_power", "columns": ["initiative_region", "resource_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"festivals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"rural_feeder_roads\")\n", "labels": {"reads": [{"table": "festivals", "columns": null}], "writes": [{"table": "rural_feeder_roads", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.device_log_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"assets_frameworks\")\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": null}], "writes": [{"table": "assets_frameworks", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT years_played, app_name FROM race LIMIT 20\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO healthcare_centers SELECT signupdate, max_temperature_f FROM brands WHERE signupdate > 407\")\n", "labels": {"reads": [{"table": "race", "columns": ["years_played", "app_name"]}, {"table": "brands", "columns": ["signupdate", "max_temperature_f"]}], "writes": [{"table": "healthcare_centers", "columns": ["signupdate", "max_temperature_f"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO culturalevents SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stg.cart_item_full SELECT albumname, donation_id, age_group_id FROM mart.mart_member_point_df WHERE albumname > 41\"\n", "labels": {"reads": [{"table": "mart.mart_member_point_df", "columns": ["albumname", "donation_id", "age_group_id"]}], "writes": [{"table": "stg.cart_item_full", "columns": ["albumname", "donation_id", "age_group_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.response_received_date > 200).all()\n# src table: document_locations\nengine.execute(\"INSERT INTO trains SELECT * FROM document_locations\")\n", "labels": {"reads": [{"table": "document_locations", "columns": null}], "writes": [{"table": "trains", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO manufacturersustainability SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO concentrateprices SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT headquarters, incident_category FROM atlantic_ocean_fish LIMIT 239;\nINSERT INTO taxi_data SELECT matchdate, stu_phone, membership_amount FROM gamestats WHERE matchdate > 180;\nSQL\n", "labels": {"reads": [{"table": "atlantic_ocean_fish", "columns": ["headquarters", "incident_category"]}, {"table": "gamestats", "columns": ["matchdate", "stu_phone", "membership_amount"]}], "writes": [{"table": "taxi_data", "columns": ["matchdate", "stu_phone", "membership_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_adaptation\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bioprocesses\")\n", "labels": {"reads": [{"table": "climate_adaptation", "columns": null}], "writes": [{"table": "bioprocesses", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO wildlife SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table locations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "locations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO crop_temperature SELECT course_name, founder_country, attorneyid, esg_factor FROM artsheritage WHERE course_name > 420\"\n", "labels": {"reads": [{"table": "artsheritage", "columns": ["course_name", "founder_country", "attorneyid", "esg_factor"]}], "writes": [{"table": "crop_temperature", "columns": ["course_name", "founder_country", "attorneyid", "esg_factor"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO budget_allocations SELECT opening_year, first_year FROM clothingsales WHERE opening_year > 402;\nEOF\n", "labels": {"reads": [{"table": "clothingsales", "columns": ["opening_year", "first_year"]}], "writes": [{"table": "budget_allocations", "columns": ["opening_year", "first_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"player_coach\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"navalvessels\")\n", "labels": {"reads": [{"table": "player_coach", "columns": null}], "writes": [{"table": "navalvessels", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.risk_score_df\", conn)\ndf.to_sql(\"student_mental_health\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": null}], "writes": [{"table": "student_mental_health", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO consumer SELECT a.loan_amount, b.staff_id FROM storage_tech a JOIN sports b ON a.materialname = b.materialname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "storage_tech", "columns": null}, {"table": "sports", "columns": null}], "writes": [{"table": "consumer", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"has_allergy\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"funding_rounds\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": null}], "writes": [{"table": "funding_rounds", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"facility_production\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "facility_production", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO item_prices SELECT day_of_week, policyid, collection_id FROM drugs WHERE day_of_week > 398\"\n", "labels": {"reads": [{"table": "drugs", "columns": ["day_of_week", "policyid", "collection_id"]}], "writes": [{"table": "item_prices", "columns": ["day_of_week", "policyid", "collection_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO apartments (membership, location_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "apartments", "columns": ["membership", "location_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT certified, generation_date FROM premises\", engine)\nif not rows:\n    logger.warning('empty result')\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"leo_missions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "premises", "columns": ["certified", "generation_date"]}], "writes": [{"table": "leo_missions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"species_forests\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"material_production\")\n", "labels": {"reads": [{"table": "species_forests", "columns": null}], "writes": [{"table": "material_production", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shariahfinance depends on spacecraft_manufacturing\ndbt run --models shariahfinance --vars '{\"src\":\"spacecraft_manufacturing\"}'\n", "labels": {"reads": [{"table": "spacecraft_manufacturing", "columns": null}], "writes": [{"table": "shariahfinance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO co_ownership SELECT a.business_zone, b.development_name FROM workforce_development_programs a JOIN communityhealthworkerscanada b ON a.class_senator_vote = b.class_senator_vote\"\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": null}, {"table": "communityhealthworkerscanada", "columns": null}], "writes": [{"table": "co_ownership", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"draft_copies\")\npush_to_warehouse(df, \"restorative_justice_center\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}], "writes": [{"table": "restorative_justice_center", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO skills SELECT energy_efficiency_kwh_m2_year, did, stu_gpa FROM artists_valuation WHERE energy_efficiency_kwh_m2_year > 126\"\n", "labels": {"reads": [{"table": "artists_valuation", "columns": ["energy_efficiency_kwh_m2_year", "did", "stu_gpa"]}], "writes": [{"table": "skills", "columns": ["energy_efficiency_kwh_m2_year", "did", "stu_gpa"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO host (case_status, share_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "host", "columns": ["case_status", "share_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"route_fares\").toPandas()\ndf[[\"video_id\", \"headquarter\"]].to_sql(\"maintenance\", engine, index=False)\n", "labels": {"reads": [{"table": "route_fares", "columns": null}], "writes": [{"table": "maintenance", "columns": ["video_id", "headquarter"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO educationprograms SELECT a.fastestlapspeed, b.workshop_name FROM apartments a JOIN bi.bi_sessions_daily b ON a.cname = b.cname\"\n", "labels": {"reads": [{"table": "apartments", "columns": null}, {"table": "bi.bi_sessions_daily", "columns": null}], "writes": [{"table": "educationprograms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO landfills (resource_type, daily_consumption) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "landfills", "columns": ["resource_type", "daily_consumption"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.stg_coupon_use_di SELECT * FROM legacy\ncur.execute(\"SELECT creation, settlement_amount FROM expenses LIMIT 221\")\n", "labels": {"reads": [{"table": "expenses", "columns": ["creation", "settlement_amount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.product_stock_number > 37).all()\n# src table: crime_stats\nengine.execute(\"INSERT INTO stellar_transactions SELECT * FROM crime_stats\")\n", "labels": {"reads": [{"table": "crime_stats", "columns": null}], "writes": [{"table": "stellar_transactions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"africa_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"weather\")\n", "labels": {"reads": [{"table": "africa_projects", "columns": null}], "writes": [{"table": "weather", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table facility --target-dir /tmp/land\n", "labels": {"reads": [{"table": "facility", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM drills\", conn)\ndf.to_sql(\"behavior_incident\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "drills", "columns": null}], "writes": [{"table": "behavior_incident", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM research\"\n", "labels": {"reads": [{"table": "research", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM field5\"\n", "labels": {"reads": [{"table": "field5", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"customers_cards\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"circular_economy_initiatives\")\n", "labels": {"reads": [{"table": "customers_cards", "columns": null}], "writes": [{"table": "circular_economy_initiatives", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO wta_serves SELECT mental_health_score, played, hospital_id FROM autonomous_testing WHERE mental_health_score > 163\"], check=True)\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": ["mental_health_score", "played", "hospital_id"]}], "writes": [{"table": "wta_serves", "columns": ["mental_health_score", "played", "hospital_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attendee_demographics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"video_games\")\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": null}], "writes": [{"table": "video_games", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table wind_farms --target-dir /tmp/land\n", "labels": {"reads": [{"table": "wind_farms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.inventory_daily SELECT laborproductivity, neighborhoodid, start, union_members FROM zipcodes WHERE laborproductivity > 206\"\n", "labels": {"reads": [{"table": "zipcodes", "columns": ["laborproductivity", "neighborhoodid", "start", "union_members"]}], "writes": [{"table": "bi.inventory_daily", "columns": ["laborproductivity", "neighborhoodid", "start", "union_members"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.inventory_daily\").toPandas()\ndf[[\"contract_address\", \"job\"]].to_sql(\"claims\", engine, index=False)\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": null}], "writes": [{"table": "claims", "columns": ["contract_address", "job"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"team_members\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "team_members", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO veterans SELECT dose, marketing_region_descriptrion, truck_licence_number FROM ads.ads_campaigns_full WHERE dose > 82\"\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": ["dose", "marketing_region_descriptrion", "truck_licence_number"]}], "writes": [{"table": "veterans", "columns": ["dose", "marketing_region_descriptrion", "truck_licence_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO hair_care_sales SELECT target_u_id, manufacturerid FROM seamounts WHERE target_u_id > 476\"\n", "labels": {"reads": [{"table": "seamounts", "columns": ["target_u_id", "manufacturerid"]}], "writes": [{"table": "hair_care_sales", "columns": ["target_u_id", "manufacturerid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO eia_schedule SELECT * FROM legacy\ncur.execute(\"SELECT email_address, virtual_tour_engagement_time FROM teacher_professional_development LIMIT 171\")\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": ["email_address", "virtual_tour_engagement_time"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model waste depends on hosting_city\ndbt build --select waste --vars 'source: hosting_city'\n", "labels": {"reads": [{"table": "hosting_city", "columns": null}], "writes": [{"table": "waste", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO council_tax SELECT stay, asset_make FROM trains WHERE stay > 209\"\n", "labels": {"reads": [{"table": "trains", "columns": ["stay", "asset_make"]}], "writes": [{"table": "council_tax", "columns": ["stay", "asset_make"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"infantmortalitydata\")\nsrc.write.insertInto(\"heritage_sites\", overwrite=True)\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": null}], "writes": [{"table": "heritage_sites", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_offset_initiatives SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_users_hourly\").toPandas()\ndf[[\"date_in_location_from\", \"time_of_day\"]].to_sql(\"sustainable_sourcing\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": null}], "writes": [{"table": "sustainable_sourcing", "columns": ["date_in_location_from", "time_of_day"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model accessible_tech_categories depends on communityhealthworkers\ndbt build --models accessible_tech_categories --vars 'source: communityhealthworkers'\n", "labels": {"reads": [{"table": "communityhealthworkers", "columns": null}], "writes": [{"table": "accessible_tech_categories", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT stu_dob, workshop_name FROM hotel_reviews\", engine)\nimport logging\ndf.to_sql(\"government.region\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": ["stu_dob", "workshop_name"]}], "writes": [{"table": "government.region", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"co_ownership\").toPandas()\ndf[[\"appointment_time\", \"garment_id\"]].to_sql(\"prescribes\", engine, index=False)\n", "labels": {"reads": [{"table": "co_ownership", "columns": null}], "writes": [{"table": "prescribes", "columns": ["appointment_time", "garment_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climate_data SELECT openning_year, field, policy_id, equipment_id FROM producers WHERE openning_year > 226\"\n", "labels": {"reads": [{"table": "producers", "columns": ["openning_year", "field", "policy_id", "equipment_id"]}], "writes": [{"table": "climate_data", "columns": ["openning_year", "field", "policy_id", "equipment_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tickets_3 SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"aircraft\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "aircraft", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityscores\").toPandas()\ndf[[\"product_size\", \"platform\"]].to_sql(\"ancient_artifacts\", engine, index=False)\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": [{"table": "ancient_artifacts", "columns": ["product_size", "platform"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"decentralized_applications\")\nsrc.write.insertInto(\"ods_member_point_full\", overwrite=True)\n", "labels": {"reads": [{"table": "decentralized_applications", "columns": null}], "writes": [{"table": "ods_member_point_full", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_registration\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO browser SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO strandings SELECT min_dew_point_f, code, catalog_name, consider_rate FROM sustainability_metrics WHERE min_dew_point_f > 349\")\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": ["min_dew_point_f", "code", "catalog_name", "consider_rate"]}], "writes": [{"table": "strandings", "columns": ["min_dew_point_f", "code", "catalog_name", "consider_rate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT creation_year, gameid FROM open_pedagogy_courses LIMIT 199;\nINSERT INTO timed_locations_of_things SELECT max_gust_speed_mph, squadron FROM home_game WHERE max_gust_speed_mph > 145;\nSQL\n", "labels": {"reads": [{"table": "open_pedagogy_courses", "columns": ["creation_year", "gameid"]}, {"table": "home_game", "columns": ["max_gust_speed_mph", "squadron"]}], "writes": [{"table": "timed_locations_of_things", "columns": ["max_gust_speed_mph", "squadron"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO humanitarian_aid SELECT field_id, fault_log_entry_id, vendorid FROM athlete_wellbeing WHERE field_id > 490\"\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": ["field_id", "fault_log_entry_id", "vendorid"]}], "writes": [{"table": "humanitarian_aid", "columns": ["field_id", "fault_log_entry_id", "vendorid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO water_conservation SELECT citizens, bioreactor_id, playlist_id, num_libraries FROM portfolios WHERE citizens > 277\"\n", "labels": {"reads": [{"table": "portfolios", "columns": ["citizens", "bioreactor_id", "playlist_id", "num_libraries"]}], "writes": [{"table": "water_conservation", "columns": ["citizens", "bioreactor_id", "playlist_id", "num_libraries"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO project_timelines SELECT contract_number, releaseyear, dispensary, fault_status FROM seafoodsouthafricakenya WHERE contract_number > 110\"], check=True)\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["contract_number", "releaseyear", "dispensary", "fault_status"]}], "writes": [{"table": "project_timelines", "columns": ["contract_number", "releaseyear", "dispensary", "fault_status"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart.mart_products_hourly SELECT workshop_name, exhibitions FROM mart.mart_device_log_hourly WHERE workshop_name > 282\"\n", "labels": {"reads": [{"table": "mart.mart_device_log_hourly", "columns": ["workshop_name", "exhibitions"]}], "writes": [{"table": "mart.mart_products_hourly", "columns": ["workshop_name", "exhibitions"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_distribution\").toPandas()\ndf[[\"status_of_thing_code\", \"participant\"]].to_sql(\"miningoperations\", engine, index=False)\n", "labels": {"reads": [{"table": "water_distribution", "columns": null}], "writes": [{"table": "miningoperations", "columns": ["status_of_thing_code", "participant"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gender_mf, worker FROM machinery LIMIT 169\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "machinery", "columns": ["gender_mf", "worker"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"labour_productivity\")\nsrc.write.insertInto(\"vehiclemodels\", overwrite=True)\n", "labels": {"reads": [{"table": "labour_productivity", "columns": null}], "writes": [{"table": "vehiclemodels", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_exposure_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods_exposure_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"astronautmedicaldata\")\nsrc.write.insertInto(\"defense_contractors\", overwrite=True)\n", "labels": {"reads": [{"table": "astronautmedicaldata", "columns": null}], "writes": [{"table": "defense_contractors", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"policyanalysis\").toPandas()\ndf[[\"inclusive_housing_policy\", \"end_station_id\"]].to_sql(\"mart.mart_payments_df\", engine, index=False)\n", "labels": {"reads": [{"table": "policyanalysis", "columns": null}], "writes": [{"table": "mart.mart_payments_df", "columns": ["inclusive_housing_policy", "end_station_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ref_incident_type SELECT a.mouse_id, b.text FROM ticketsales a JOIN player_coach b ON a.attack_count = b.attack_count\"\n", "labels": {"reads": [{"table": "ticketsales", "columns": null}, {"table": "player_coach", "columns": null}], "writes": [{"table": "ref_incident_type", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO excavation_sites SELECT data_usage, course_id FROM buildings WHERE data_usage > 287;\nEOF\n", "labels": {"reads": [{"table": "buildings", "columns": ["data_usage", "course_id"]}], "writes": [{"table": "excavation_sites", "columns": ["data_usage", "course_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO retailerg SELECT a.player, b.fleet_series FROM trenches a JOIN department_publications b ON a.sale_revenue = b.sale_revenue\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trenches", "columns": null}, {"table": "department_publications", "columns": null}], "writes": [{"table": "retailerg", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fair_trade_brands SELECT host_city, contract_type, subject_area_id FROM wind_energy WHERE host_city > 257\"], check=True)\n", "labels": {"reads": [{"table": "wind_energy", "columns": ["host_city", "contract_type", "subject_area_id"]}], "writes": [{"table": "fair_trade_brands", "columns": ["host_city", "contract_type", "subject_area_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"feed\")\nsrc.write.insertInto(\"bi.bi_inventory_di\", overwrite=True)\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": [{"table": "bi.bi_inventory_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"tours\")\nsrc.write.insertInto(\"drilling_rigs\", overwrite=True)\n", "labels": {"reads": [{"table": "tours", "columns": null}], "writes": [{"table": "drilling_rigs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO defenseprojects SELECT recipient_id, rows, restypename, state_province_county FROM dwd.dwd_member_point_full WHERE recipient_id > 280\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_full", "columns": ["recipient_id", "rows", "restypename", "state_province_county"]}], "writes": [{"table": "defenseprojects", "columns": ["recipient_id", "rows", "restypename", "state_province_county"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO hospitals SELECT payment_method, form_id, tournament_id FROM dwd.coupon_use_daily WHERE payment_method > 166\"\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": ["payment_method", "form_id", "tournament_id"]}], "writes": [{"table": "hospitals", "columns": ["payment_method", "form_id", "tournament_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO co2_emission (reason, sales_billion) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "co2_emission", "columns": ["reason", "sales_billion"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_orders_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bi.bi_orders_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM europium_exports\", conn)\ndf.to_sql(\"vessel_positions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "europium_exports", "columns": null}], "writes": [{"table": "vessel_positions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT intervention_type, customer_country FROM culturalcompetencytrainings LIMIT 10;\nINSERT INTO contract_negotiations_un SELECT date_of_latest_revision, eliminated_by FROM smart_cities WHERE date_of_latest_revision > 245;\nSQL\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": ["intervention_type", "customer_country"]}, {"table": "smart_cities", "columns": ["date_of_latest_revision", "eliminated_by"]}], "writes": [{"table": "contract_negotiations_un", "columns": ["date_of_latest_revision", "eliminated_by"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ma_inspections SELECT veteran_unemployment_rate, fleet_id FROM management WHERE veteran_unemployment_rate > 68\"], check=True)\n", "labels": {"reads": [{"table": "management", "columns": ["veteran_unemployment_rate", "fleet_id"]}], "writes": [{"table": "ma_inspections", "columns": ["veteran_unemployment_rate", "fleet_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO budget_allocations SELECT monthlyactiveusers, regionid FROM industry_funding WHERE monthlyactiveusers > 286\"\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["monthlyactiveusers", "regionid"]}], "writes": [{"table": "budget_allocations", "columns": ["monthlyactiveusers", "regionid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"job_postings\")\nsave_to_output(df, \"staff_roles\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "job_postings", "columns": null}], "writes": [{"table": "staff_roles", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO cultural_heritage (policyname, review_rating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "cultural_heritage", "columns": ["policyname", "review_rating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT strain_name, personnel FROM workplaces LIMIT 211\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "workplaces", "columns": ["strain_name", "personnel"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO instructor SELECT shipping_mode, product_details, volunteerdate FROM mart.mart_device_log_delta WHERE shipping_mode > 482\")\n", "labels": {"reads": [{"table": "mart.mart_device_log_delta", "columns": ["shipping_mode", "product_details", "volunteerdate"]}], "writes": [{"table": "instructor", "columns": ["shipping_mode", "product_details", "volunteerdate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO seafood (image_url, staff_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "seafood", "columns": ["image_url", "staff_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM hires\", conn)\ndf.to_sql(\"bay_area_properties\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "hires", "columns": null}], "writes": [{"table": "bay_area_properties", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"shrimp_farms\")\nsrc.write.insertInto(\"innovation_grants\", overwrite=True)\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": null}], "writes": [{"table": "innovation_grants", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"ais\")\nwrite_to_warehouse(df, \"eventdates\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ais", "columns": null}], "writes": [{"table": "eventdates", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dishes SELECT hotel_name, store_name, sqft FROM nomination WHERE hotel_name > 461\"], check=True)\n", "labels": {"reads": [{"table": "nomination", "columns": ["hotel_name", "store_name", "sqft"]}], "writes": [{"table": "dishes", "columns": ["hotel_name", "store_name", "sqft"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT mealid, emp_fname FROM ytterbiumproduction LIMIT 266\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ytterbiumproduction", "columns": ["mealid", "emp_fname"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO facility_production SELECT category, date_stored, vegetable FROM human_resources WHERE category > 281;\nEOF\n", "labels": {"reads": [{"table": "human_resources", "columns": ["category", "date_stored", "vegetable"]}], "writes": [{"table": "facility_production", "columns": ["category", "date_stored", "vegetable"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO renewable_energy_projects (volunteer_id, range) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "renewable_energy_projects", "columns": ["volunteer_id", "range"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO patents (acc_bal, num_investments) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "patents", "columns": ["acc_bal", "num_investments"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO appliances SELECT * FROM legacy\ncur.execute(\"SELECT funding_received, classtype FROM vehiclemodels LIMIT 201\")\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": ["funding_received", "classtype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.shipments_df\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stops\")\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}], "writes": [{"table": "stops", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT nominee, compatible_since_year FROM foodsafetyrecords\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"membership_register_branch\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": ["nominee", "compatible_since_year"]}], "writes": [{"table": "membership_register_branch", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO news_views SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO expenditure SELECT a.salinity, b.amount_payment FROM whale_sightings a JOIN bridgeconstruction b ON a.line_id = b.line_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}, {"table": "bridgeconstruction", "columns": null}], "writes": [{"table": "expenditure", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO courtcases SELECT a.manufacturer, b.countryname FROM menu_items a JOIN developers b ON a.transaction_value = b.transaction_value\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "menu_items", "columns": null}, {"table": "developers", "columns": null}], "writes": [{"table": "courtcases", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO systems SELECT era, employee, sustainability_initiative_id FROM film_actor WHERE era > 197\")\n", "labels": {"reads": [{"table": "film_actor", "columns": ["era", "employee", "sustainability_initiative_id"]}], "writes": [{"table": "systems", "columns": ["era", "employee", "sustainability_initiative_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"claims_documents\")\nsave_to_sink(df, \"co2_sequestration\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "claims_documents", "columns": null}], "writes": [{"table": "co2_sequestration", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws_events_df SELECT * FROM legacy\ncur.execute(\"SELECT unique_founders, mascot FROM bi.events_delta LIMIT 49\")\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": ["unique_founders", "mascot"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT paintingid, artist_name FROM well_production LIMIT 412\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO communitycourtcases SELECT waste_id, disability_type, energy_generated FROM mental_health_parity WHERE waste_id > 167\")\n", "labels": {"reads": [{"table": "well_production", "columns": ["paintingid", "artist_name"]}, {"table": "mental_health_parity", "columns": ["waste_id", "disability_type", "energy_generated"]}], "writes": [{"table": "communitycourtcases", "columns": ["waste_id", "disability_type", "energy_generated"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO nba_games SELECT dock_count, total_donation_amount, sessiondate FROM host WHERE dock_count > 477;\nEOF\n", "labels": {"reads": [{"table": "host", "columns": ["dock_count", "total_donation_amount", "sessiondate"]}], "writes": [{"table": "nba_games", "columns": ["dock_count", "total_donation_amount", "sessiondate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO port_visits SELECT asset_details, mode, fleet_id, account_type FROM heart_rate_data WHERE asset_details > 259\"\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": ["asset_details", "mode", "fleet_id", "account_type"]}], "writes": [{"table": "port_visits", "columns": ["asset_details", "mode", "fleet_id", "account_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO schools SELECT * FROM legacy\ncur.execute(\"SELECT contract_end_date, coverage_type FROM weapons LIMIT 360\")\n", "labels": {"reads": [{"table": "weapons", "columns": ["contract_end_date", "coverage_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM flu_shots\"\n", "labels": {"reads": [{"table": "flu_shots", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM feed\"\n", "labels": {"reads": [{"table": "feed", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO biosensors.projects SELECT * FROM legacy\ncur.execute(\"SELECT quantity_sold, journalist_id FROM readership LIMIT 392\")\n", "labels": {"reads": [{"table": "readership", "columns": ["quantity_sold", "journalist_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM document_types\"\n", "labels": {"reads": [{"table": "document_types", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"investment_strategies\")\nsrc.write.insertInto(\"cultivators\", overwrite=True)\n", "labels": {"reads": [{"table": "investment_strategies", "columns": null}], "writes": [{"table": "cultivators", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT workshop_id, gname FROM budgets LIMIT 254;\nINSERT INTO latam_schema.education_budget SELECT experience_id, savingsid FROM emergencies WHERE experience_id > 459;\nSQL\n", "labels": {"reads": [{"table": "budgets", "columns": ["workshop_id", "gname"]}, {"table": "emergencies", "columns": ["experience_id", "savingsid"]}], "writes": [{"table": "latam_schema.education_budget", "columns": ["experience_id", "savingsid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dishname, issued_date FROM opendatainitiatives\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"disabilitysupportprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": ["dishname", "issued_date"]}], "writes": [{"table": "disabilitysupportprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO cargo_tracking (amount_waste, date_of_ceremony) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "cargo_tracking", "columns": ["amount_waste", "date_of_ceremony"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.events_delta SELECT * FROM legacy\ncur.execute(\"SELECT lender_id, building_id FROM playersessions LIMIT 489\")\n", "labels": {"reads": [{"table": "playersessions", "columns": ["lender_id", "building_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO voyages SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"neighborhoods\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"temperature_data\")\n", "labels": {"reads": [{"table": "neighborhoods", "columns": null}], "writes": [{"table": "temperature_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO ocean_species SELECT routeid, active_to_date, clean_date, built FROM state_energy WHERE routeid > 161\"\n", "labels": {"reads": [{"table": "state_energy", "columns": ["routeid", "active_to_date", "clean_date", "built"]}], "writes": [{"table": "ocean_species", "columns": ["routeid", "active_to_date", "clean_date", "built"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO legal_precedents SELECT financial_wellbeing_score, time_id, contractorid FROM ods.ods_payments_full WHERE financial_wellbeing_score > 289\"\n", "labels": {"reads": [{"table": "ods.ods_payments_full", "columns": ["financial_wellbeing_score", "time_id", "contractorid"]}], "writes": [{"table": "legal_precedents", "columns": ["financial_wellbeing_score", "time_id", "contractorid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM carbon_footprint\"\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mart_shipments_full SELECT habitat_name, date_incident_start FROM restaurant_revenue WHERE habitat_name > 261\"\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": ["habitat_name", "date_incident_start"]}], "writes": [{"table": "mart_shipments_full", "columns": ["habitat_name", "date_incident_start"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO facility SELECT savingsid, manufacturer_name FROM excavations WHERE savingsid > 470;\nEOF\n", "labels": {"reads": [{"table": "excavations", "columns": ["savingsid", "manufacturer_name"]}], "writes": [{"table": "facility", "columns": ["savingsid", "manufacturer_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sustainable_practices_2 SELECT gross_worldwide, typical_selling_price FROM part_faults WHERE gross_worldwide > 149;\nEOF\n", "labels": {"reads": [{"table": "part_faults", "columns": ["gross_worldwide", "typical_selling_price"]}], "writes": [{"table": "sustainable_practices_2", "columns": ["gross_worldwide", "typical_selling_price"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT manufacturer_id, grant_type FROM support_programs LIMIT 397\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "support_programs", "columns": ["manufacturer_id", "grant_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"paris_train\")\nsrc.write.insertInto(\"countryintelligenceops\", overwrite=True)\n", "labels": {"reads": [{"table": "paris_train", "columns": null}], "writes": [{"table": "countryintelligenceops", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"travel_advisory\").toPandas()\ndf[[\"replacement_cost\", \"transaction_amount\"]].to_sql(\"virtual_tour_revenue\", engine, index=False)\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "virtual_tour_revenue", "columns": ["replacement_cost", "transaction_amount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table college --columns strat_id,individual_last_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "college", "columns": ["strat_id", "individual_last_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO reo_production SELECT project_number, programid, took_office FROM dwd.dwd_cart_item_di WHERE project_number > 88\"\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": ["project_number", "programid", "took_office"]}], "writes": [{"table": "reo_production", "columns": ["project_number", "programid", "took_office"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"workers\")\nsrc.write.insertInto(\"menu_item\", overwrite=True)\n", "labels": {"reads": [{"table": "workers", "columns": null}], "writes": [{"table": "menu_item", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.event_type > 14).all()\n# src table: exhibitiondetails\nengine.execute(\"INSERT INTO pollution_initiatives SELECT * FROM exhibitiondetails\")\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": [{"table": "pollution_initiatives", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO public_transportation_sydney SELECT a.speciesid, b.bike_id FROM arcticocean a JOIN container_ships b ON a.vaccine_type = b.vaccine_type\"\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}, {"table": "container_ships", "columns": null}], "writes": [{"table": "public_transportation_sydney", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"reservoirs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd.products_hourly\")\n", "labels": {"reads": [{"table": "reservoirs", "columns": null}], "writes": [{"table": "dwd.products_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"urban_agriculture_initiatives\")\npush_to_warehouse(df, \"overwatch_scores\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": null}], "writes": [{"table": "overwatch_scores", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table tencel_sources --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO open_pedagogy_exam SELECT crs_credit, assessmentname FROM pitstops WHERE crs_credit > 218\"], check=True)\n", "labels": {"reads": [{"table": "pitstops", "columns": ["crs_credit", "assessmentname"]}], "writes": [{"table": "open_pedagogy_exam", "columns": ["crs_credit", "assessmentname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.ads_payments SELECT dish_name, subject FROM genre_songs WHERE dish_name > 266\"], check=True)\n", "labels": {"reads": [{"table": "genre_songs", "columns": ["dish_name", "subject"]}], "writes": [{"table": "ads.ads_payments", "columns": ["dish_name", "subject"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT health_equity_metric_3, time_month FROM market_share\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ndf.to_sql(\"precipitation_data\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "market_share", "columns": ["health_equity_metric_3", "time_month"]}], "writes": [{"table": "precipitation_data", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"philadelphia_police_emergencies\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"asia_events\")\nsrc.write.insertInto(\"explainable_ai\", overwrite=True)\n", "labels": {"reads": [{"table": "asia_events", "columns": null}], "writes": [{"table": "explainable_ai", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM innovation_metrics\"\n", "labels": {"reads": [{"table": "innovation_metrics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO bi.bi_payments_df SELECT city_population, organizationid, funding_source, training_id FROM industry_funding WHERE city_population > 336\")\n", "labels": {"reads": [{"table": "industry_funding", "columns": ["city_population", "organizationid", "funding_source", "training_id"]}], "writes": [{"table": "bi.bi_payments_df", "columns": ["city_population", "organizationid", "funding_source", "training_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.songid > 283).all()\n# src table: ref_detention_type\nengine.execute(\"INSERT INTO conservation_projects SELECT * FROM ref_detention_type\")\n", "labels": {"reads": [{"table": "ref_detention_type", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO available_policies SELECT a.tonnage, b.marketing_region_code FROM stg.stg_users a JOIN org_donation b ON a.investor_name = b.investor_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stg.stg_users", "columns": null}, {"table": "org_donation", "columns": null}], "writes": [{"table": "available_policies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_campaigns_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"fans\")\n", "labels": {"reads": [{"table": "stg.stg_campaigns_hourly", "columns": null}], "writes": [{"table": "fans", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ais SELECT contract_start_date, industry_4_0 FROM public_transportation_sydney WHERE contract_start_date > 400;\nEOF\n", "labels": {"reads": [{"table": "public_transportation_sydney", "columns": ["contract_start_date", "industry_4_0"]}], "writes": [{"table": "ais", "columns": ["contract_start_date", "industry_4_0"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"timber_sales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "timber_sales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table workout_sessions --columns topic,attendees --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "workout_sessions", "columns": ["topic", "attendees"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO contract_negotiations SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO state_budget SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO news_report (stu_fname, reo_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "news_report", "columns": ["stu_fname", "reo_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO carbon_prices_3 (inspection_date, installation_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "carbon_prices_3", "columns": ["inspection_date", "installation_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT operation_count, product_category FROM elimination LIMIT 202\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "elimination", "columns": ["operation_count", "product_category"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table operation --columns region_code,card_type_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "operation", "columns": ["region_code", "card_type_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climber\").toPandas()\ndf[[\"monthlyactiveusers\", \"acc_bal\"]].to_sql(\"bikerental\", engine, index=False)\n", "labels": {"reads": [{"table": "climber", "columns": null}], "writes": [{"table": "bikerental", "columns": ["monthlyactiveusers", "acc_bal"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fireincidents SELECT facid, contributorid, gamename, reported_by_staff_id FROM academic_publications WHERE facid > 368\"], check=True)\n", "labels": {"reads": [{"table": "academic_publications", "columns": ["facid", "contributorid", "gamename", "reported_by_staff_id"]}], "writes": [{"table": "fireincidents", "columns": ["facid", "contributorid", "gamename", "reported_by_staff_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT delivery_time, judge_state FROM document_sections_images LIMIT 387\")\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO ads.inventory_di SELECT claim_header_id, singer_id, time_hour FROM tech_workers_union WHERE claim_header_id > 32\")\n", "labels": {"reads": [{"table": "document_sections_images", "columns": ["delivery_time", "judge_state"]}, {"table": "tech_workers_union", "columns": ["claim_header_id", "singer_id", "time_hour"]}], "writes": [{"table": "ads.inventory_di", "columns": ["claim_header_id", "singer_id", "time_hour"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO categories SELECT totalprice, sport, profits_billion FROM crimes WHERE totalprice > 258\"\n", "labels": {"reads": [{"table": "crimes", "columns": ["totalprice", "sport", "profits_billion"]}], "writes": [{"table": "categories", "columns": ["totalprice", "sport", "profits_billion"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM equipment_sales\", conn)\ndf.to_sql(\"birds\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "equipment_sales", "columns": null}], "writes": [{"table": "birds", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT extraction_amount, oil_production_q4_2021 FROM disaster_response_donations LIMIT 62;\nINSERT INTO bi.device_log SELECT strainname, guest_id, record_id FROM economic_diversification WHERE strainname > 359;\nSQL\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": ["extraction_amount", "oil_production_q4_2021"]}, {"table": "economic_diversification", "columns": ["strainname", "guest_id", "record_id"]}], "writes": [{"table": "bi.device_log", "columns": ["strainname", "guest_id", "record_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO fashion_trend_data SELECT * FROM legacy\ncur.execute(\"SELECT funding_year, excavation_site FROM space_missions_2 LIMIT 468\")\n", "labels": {"reads": [{"table": "space_missions_2", "columns": ["funding_year", "excavation_site"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mediterranean_salinity SELECT * FROM legacy\ncur.execute(\"SELECT technique, monthly_rental FROM view_unit_status LIMIT 118\")\n", "labels": {"reads": [{"table": "view_unit_status", "columns": ["technique", "monthly_rental"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO impact_investments (num_cases, area_sqkm) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "impact_investments", "columns": ["num_cases", "area_sqkm"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO climate_communication_projects SELECT date_opened, sustainability_id FROM militarycyberops WHERE date_opened > 499\")\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["date_opened", "sustainability_id"]}], "writes": [{"table": "climate_communication_projects", "columns": ["date_opened", "sustainability_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO restaurant_type (units_owned, heritage_site) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "restaurant_type", "columns": ["units_owned", "heritage_site"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO user_stats (booked_amount, artifact_weight) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "user_stats", "columns": ["booked_amount", "artifact_weight"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO animal_species SELECT a.check_in_date, b.submission_id FROM tourism_activities a JOIN bridge b ON a.bioprocess_name = b.bioprocess_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tourism_activities", "columns": null}, {"table": "bridge", "columns": null}], "writes": [{"table": "animal_species", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO farmers (partid, catalog_entry_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "farmers", "columns": ["partid", "catalog_entry_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM party\"\n", "labels": {"reads": [{"table": "party", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table organic_products --target-dir /tmp/land\n", "labels": {"reads": [{"table": "organic_products", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table languages --columns governor,hire_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "languages", "columns": ["governor", "hire_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"vessels\")\ndump_to_store(df, \"socialimpactinvestments\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "vessels", "columns": null}], "writes": [{"table": "socialimpactinvestments", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table property_community --columns assets_billion,clublocation --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "property_community", "columns": ["assets_billion", "clublocation"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table socialimpactinvestments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM collectivebargaining\"\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ecohousing\"\n", "labels": {"reads": [{"table": "ecohousing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"arcticocean\")\nwrite_to_warehouse(df, \"document_functional_areas\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": [{"table": "document_functional_areas", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO urban_agriculture_initiatives SELECT a.cultural_competency_score, b.factory_id FROM labor_statistics a JOIN spacecraft_manufacturing b ON a.treatment_type = b.treatment_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "labor_statistics", "columns": null}, {"table": "spacecraft_manufacturing", "columns": null}], "writes": [{"table": "urban_agriculture_initiatives", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart_events_full SELECT fleet_id, capacity_percentage, concert_name FROM attendee_demographics WHERE fleet_id > 18\"\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": ["fleet_id", "capacity_percentage", "concert_name"]}], "writes": [{"table": "mart_events_full", "columns": ["fleet_id", "capacity_percentage", "concert_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT vin, reports_to FROM member_details\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"ods.ods_risk_score_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "member_details", "columns": ["vin", "reports_to"]}], "writes": [{"table": "ods.ods_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT i_id, events FROM circular_economy LIMIT 275\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "circular_economy", "columns": ["i_id", "events"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO atlantic_ocean_fish SELECT * FROM legacy\ncur.execute(\"SELECT vessel, device_name FROM wildlife_sanctuaries LIMIT 92\")\n", "labels": {"reads": [{"table": "wildlife_sanctuaries", "columns": ["vessel", "device_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"health_equity_metrics\")\nsave_to_warehouse(df, \"dws.dws_member_point_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "health_equity_metrics", "columns": null}], "writes": [{"table": "dws.dws_member_point_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dw_users_full (workout_name, region_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dw_users_full", "columns": ["workout_name", "region_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"traffic_violations\")\nsink_to_sink(df, \"high_risk\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "traffic_violations", "columns": null}], "writes": [{"table": "high_risk", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ads_orders depends on skills\ndbt run --models ads_orders --vars '{\"src\":\"skills\"}'\n", "labels": {"reads": [{"table": "skills", "columns": null}], "writes": [{"table": "ads_orders", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT occupancy_rate, other_details FROM student_access\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"bank_info\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "student_access", "columns": ["occupancy_rate", "other_details"]}], "writes": [{"table": "bank_info", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO vessels_2 SELECT task_details, destruction_authorised_by_employee_id, min_dew_point_f FROM postseason WHERE task_details > 441\")\n", "labels": {"reads": [{"table": "postseason", "columns": ["task_details", "destruction_authorised_by_employee_id", "min_dew_point_f"]}], "writes": [{"table": "vessels_2", "columns": ["task_details", "destruction_authorised_by_employee_id", "min_dew_point_f"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT license_type, subscriber_type FROM spacecraft_manufacturers\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"apartment_buildings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "spacecraft_manufacturers", "columns": ["license_type", "subscriber_type"]}], "writes": [{"table": "apartment_buildings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT ironid, accommodation_id FROM platform_production LIMIT 300\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO workforce_development SELECT co_id, budget FROM healthcare WHERE co_id > 242\")\n", "labels": {"reads": [{"table": "platform_production", "columns": ["ironid", "accommodation_id"]}, {"table": "healthcare", "columns": ["co_id", "budget"]}], "writes": [{"table": "workforce_development", "columns": ["co_id", "budget"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO donors_region (date_valid_to, production_cost) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "donors_region", "columns": ["date_valid_to", "production_cost"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT ethnicity, trade FROM justice_schemas.legal_tech_providers LIMIT 197;\nINSERT INTO posts_per_day SELECT engagementid, treatment_date, shipmentid FROM creative_ai_applications WHERE engagementid > 494;\nSQL\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": ["ethnicity", "trade"]}, {"table": "creative_ai_applications", "columns": ["engagementid", "treatment_date", "shipmentid"]}], "writes": [{"table": "posts_per_day", "columns": ["engagementid", "treatment_date", "shipmentid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.stg_users_full --columns video_id,strainname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_users_full", "columns": ["video_id", "strainname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO haircaresales SELECT total, cause_name, workshop_name, users_engaged FROM supplier_ethics WHERE total > 258\"\n", "labels": {"reads": [{"table": "supplier_ethics", "columns": ["total", "cause_name", "workshop_name", "users_engaged"]}], "writes": [{"table": "haircaresales", "columns": ["total", "cause_name", "workshop_name", "users_engaged"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT dockingid, other_item_details FROM customer_policies LIMIT 127\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO assignedto SELECT eventdate, productionrate, effort_name, budget_allocated FROM ads.risk_score WHERE eventdate > 317\")\n", "labels": {"reads": [{"table": "customer_policies", "columns": ["dockingid", "other_item_details"]}, {"table": "ads.risk_score", "columns": ["eventdate", "productionrate", "effort_name", "budget_allocated"]}], "writes": [{"table": "assignedto", "columns": ["eventdate", "productionrate", "effort_name", "budget_allocated"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO reporters SELECT delivery_time, claimamount FROM employee WHERE delivery_time > 261\"\n", "labels": {"reads": [{"table": "employee", "columns": ["delivery_time", "claimamount"]}], "writes": [{"table": "reporters", "columns": ["delivery_time", "claimamount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.venue_id > 250).all()\n# src table: student_access\nengine.execute(\"INSERT INTO vehicle_counts SELECT * FROM student_access\")\n", "labels": {"reads": [{"table": "student_access", "columns": null}], "writes": [{"table": "vehicle_counts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT amenity_name, treatment_type FROM wells LIMIT 96\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "wells", "columns": ["amenity_name", "treatment_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO bi_refunds_daily SELECT channel_code, living_wage, dose, balance FROM wastegeneration WHERE channel_code > 127\")\n", "labels": {"reads": [{"table": "wastegeneration", "columns": ["channel_code", "living_wage", "dose", "balance"]}], "writes": [{"table": "bi_refunds_daily", "columns": ["channel_code", "living_wage", "dose", "balance"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model communityevents depends on trainmaintenance\ndbt run --select communityevents --vars 'source: trainmaintenance'\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": null}], "writes": [{"table": "communityevents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO workout SELECT medical_risk, itemname, languageid FROM production WHERE medical_risk > 124\"\n", "labels": {"reads": [{"table": "production", "columns": ["medical_risk", "itemname", "languageid"]}], "writes": [{"table": "workout", "columns": ["medical_risk", "itemname", "languageid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO courses SELECT destination_state, vehicle_id FROM stg.stg_users_daily WHERE destination_state > 368;\nEOF\n", "labels": {"reads": [{"table": "stg.stg_users_daily", "columns": ["destination_state", "vehicle_id"]}], "writes": [{"table": "courses", "columns": ["destination_state", "vehicle_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ods.ods_sessions_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.ods_sessions_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cuisine SELECT other_characteristic_details, copy_number, speed, post_date FROM teaches WHERE other_characteristic_details > 160\"], check=True)\n", "labels": {"reads": [{"table": "teaches", "columns": ["other_characteristic_details", "copy_number", "speed", "post_date"]}], "writes": [{"table": "cuisine", "columns": ["other_characteristic_details", "copy_number", "speed", "post_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sellingprice > 374).all()\n# src table: epl_teams\nengine.execute(\"INSERT INTO droughthistory SELECT * FROM epl_teams\")\n", "labels": {"reads": [{"table": "epl_teams", "columns": null}], "writes": [{"table": "droughthistory", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO carbon_footprint SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO uniteddefense.equipmentsales SELECT a.oil_volume, b.ai_customer_service FROM fabricdata a JOIN ods.ods_member_point_delta b ON a.created_date = b.created_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fabricdata", "columns": null}, {"table": "ods.ods_member_point_delta", "columns": null}], "writes": [{"table": "uniteddefense.equipmentsales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"aquatic_farms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tv_shows\")\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": null}], "writes": [{"table": "tv_shows", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO customer_month SELECT container_id, anomaly, injured FROM impact_asia WHERE container_id > 111;\nEOF\n", "labels": {"reads": [{"table": "impact_asia", "columns": ["container_id", "anomaly", "injured"]}], "writes": [{"table": "customer_month", "columns": ["container_id", "anomaly", "injured"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO languages SELECT fairness_score, fan_name, model_id, response_type FROM immunization WHERE fairness_score > 174\"\n", "labels": {"reads": [{"table": "immunization", "columns": ["fairness_score", "fan_name", "model_id", "response_type"]}], "writes": [{"table": "languages", "columns": ["fairness_score", "fan_name", "model_id", "response_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO bi.bi_exposure_hourly SELECT shipment_tracking_number, total FROM communityhealthworkers WHERE shipment_tracking_number > 487;\nEOF\n", "labels": {"reads": [{"table": "communityhealthworkers", "columns": ["shipment_tracking_number", "total"]}], "writes": [{"table": "bi.bi_exposure_hourly", "columns": ["shipment_tracking_number", "total"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"community_events\")\nsave_to_sink(df, \"animal_population\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "community_events", "columns": null}], "writes": [{"table": "animal_population", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.stg_users SELECT a.section_title, b.appointment_time FROM legislation a JOIN mediatype b ON a.acc_type = b.acc_type\"\n", "labels": {"reads": [{"table": "legislation", "columns": null}, {"table": "mediatype", "columns": null}], "writes": [{"table": "stg.stg_users", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT plant, policy FROM project_issues LIMIT 130\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "project_issues", "columns": ["plant", "policy"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO financial_capability SELECT crime_rate, sales_billion FROM intelligence_agency WHERE crime_rate > 189;\nEOF\n", "labels": {"reads": [{"table": "intelligence_agency", "columns": ["crime_rate", "sales_billion"]}], "writes": [{"table": "financial_capability", "columns": ["crime_rate", "sales_billion"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM emergency_categories\"\n", "labels": {"reads": [{"table": "emergency_categories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO property_community SELECT program_id, clubdesc, interest_group, trips FROM sustainable_sourcing WHERE program_id > 193\"\n", "labels": {"reads": [{"table": "sustainable_sourcing", "columns": ["program_id", "clubdesc", "interest_group", "trips"]}], "writes": [{"table": "property_community", "columns": ["program_id", "clubdesc", "interest_group", "trips"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table league --columns num_attendees,wheels --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "league", "columns": ["num_attendees", "wheels"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM incarcerated\", conn)\ndf.to_sql(\"sites_me\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "incarcerated", "columns": null}], "writes": [{"table": "sites_me", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO flu_shots SELECT excavationid, forest_id FROM e_scooter_trips WHERE excavationid > 149\"\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": ["excavationid", "forest_id"]}], "writes": [{"table": "flu_shots", "columns": ["excavationid", "forest_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table artifact_analysis --target-dir /tmp/land\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical_production_5\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"train_station\")\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": [{"table": "train_station", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO program_budget (node_id, roomtype) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "program_budget", "columns": ["node_id", "roomtype"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO militaryinnovations SELECT museum_name, sustainability_certified, refugee_id, investment FROM ingredient_sourcing WHERE museum_name > 365\"\n", "labels": {"reads": [{"table": "ingredient_sourcing", "columns": ["museum_name", "sustainability_certified", "refugee_id", "investment"]}], "writes": [{"table": "militaryinnovations", "columns": ["museum_name", "sustainability_certified", "refugee_id", "investment"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.change_date > 301).all()\n# src table: ads.risk_score\nengine.execute(\"INSERT INTO artworksales SELECT * FROM ads.risk_score\")\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": null}], "writes": [{"table": "artworksales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dws.payments_delta --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.payments_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"show\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"guests\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "guests", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO concentrateprices SELECT a.attendance, b.calories FROM human_resources a JOIN ai_safety b ON a.plantid = b.plantid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "human_resources", "columns": null}, {"table": "ai_safety", "columns": null}], "writes": [{"table": "concentrateprices", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vocals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vocals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"provinces\").toPandas()\ndf[[\"account_type\", \"sqft\"]].to_sql(\"artprograms\", engine, index=False)\n", "labels": {"reads": [{"table": "provinces", "columns": null}], "writes": [{"table": "artprograms", "columns": ["account_type", "sqft"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.org_size > 1).all()\n# src table: dw.dw_risk_score_full\nengine.execute(\"INSERT INTO sustainable_tourism_practices SELECT * FROM dw.dw_risk_score_full\")\n", "labels": {"reads": [{"table": "dw.dw_risk_score_full", "columns": null}], "writes": [{"table": "sustainable_tourism_practices", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"online_travel_agency\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "online_travel_agency", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model customer_contact_channels depends on recycledmaterialsgarments\ndbt run -s customer_contact_channels --vars '{\"source_table\":\"recycledmaterialsgarments\"}'\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": null}], "writes": [{"table": "customer_contact_channels", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model container depends on autonomous_testing\ndbt run --models container --vars '{\"source_table\":\"autonomous_testing\"}'\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": null}], "writes": [{"table": "container", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO product_revenue SELECT mining_operation_id, floors, review_text FROM arcticocean WHERE mining_operation_id > 500;\nEOF\n", "labels": {"reads": [{"table": "arcticocean", "columns": ["mining_operation_id", "floors", "review_text"]}], "writes": [{"table": "product_revenue", "columns": ["mining_operation_id", "floors", "review_text"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO pilot SELECT a.constructorid, b.style FROM platformstats a JOIN disabilitysupportprograms b ON a.capacity_mw = b.capacity_mw\"\n", "labels": {"reads": [{"table": "platformstats", "columns": null}, {"table": "disabilitysupportprograms", "columns": null}], "writes": [{"table": "pilot", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model broadband_customers_global depends on rural_feeder_roads\ndbt build -s broadband_customers_global --vars 'source: rural_feeder_roads'\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": null}], "writes": [{"table": "broadband_customers_global", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model categories depends on maintenance\ndbt run --select categories --vars 'source: maintenance'\n", "labels": {"reads": [{"table": "maintenance", "columns": null}], "writes": [{"table": "categories", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO india_solar_power SELECT * FROM legacy\ncur.execute(\"SELECT issue, pilot_id FROM mart.campaigns_di LIMIT 149\")\n", "labels": {"reads": [{"table": "mart.campaigns_di", "columns": ["issue", "pilot_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO train_maintenance SELECT vesselname, line_1_number_building, railway_id FROM ads_exposure_hourly WHERE vesselname > 292\"\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["vesselname", "line_1_number_building", "railway_id"]}], "writes": [{"table": "train_maintenance", "columns": ["vesselname", "line_1_number_building", "railway_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stateinfrastructure\")\nsrc.write.insertInto(\"safety_research\", overwrite=True)\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": null}], "writes": [{"table": "safety_research", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_refunds_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"acidification_data\")\n", "labels": {"reads": [{"table": "mart.mart_refunds_hourly", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO block (merchandise_id, enr) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "block", "columns": ["merchandise_id", "enr"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"feedback\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.mart_coupon_use_full\")\n", "labels": {"reads": [{"table": "feedback", "columns": null}], "writes": [{"table": "mart.mart_coupon_use_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.campaigns_di SELECT initiativeid, shippeddate, nutrient_level, practicename FROM user_interests WHERE initiativeid > 10\"], check=True)\n", "labels": {"reads": [{"table": "user_interests", "columns": ["initiativeid", "shippeddate", "nutrient_level", "practicename"]}], "writes": [{"table": "mart.campaigns_di", "columns": ["initiativeid", "shippeddate", "nutrient_level", "practicename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT wage, subject_area_id FROM item_inventory\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"culturalevents\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "item_inventory", "columns": ["wage", "subject_area_id"]}], "writes": [{"table": "culturalevents", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"assets\").toPandas()\ndf[[\"individual_name\", \"cause_id\"]].to_sql(\"train_maintenance\", engine, index=False)\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": [{"table": "train_maintenance", "columns": ["individual_name", "cause_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.characteristic_data_type > 115).all()\n# src table: ods.clicks_delta\nengine.execute(\"INSERT INTO patient SELECT * FROM ods.clicks_delta\")\n", "labels": {"reads": [{"table": "ods.clicks_delta", "columns": null}], "writes": [{"table": "patient", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamesales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gamesales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.dwd_events_delta\", conn)\ndf.to_sql(\"student_access\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}], "writes": [{"table": "student_access", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT amount_claimed, performance_id FROM fishcaught LIMIT 253\")\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO cybersecuritybudget SELECT individual_last_name, precipitation FROM city_properties WHERE individual_last_name > 106\")\n", "labels": {"reads": [{"table": "fishcaught", "columns": ["amount_claimed", "performance_id"]}, {"table": "city_properties", "columns": ["individual_last_name", "precipitation"]}], "writes": [{"table": "cybersecuritybudget", "columns": ["individual_last_name", "precipitation"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO wind_projects SELECT transaction_date, stateid, journalist_id, satellite FROM carbon_prices_3 WHERE transaction_date > 449;\nEOF\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["transaction_date", "stateid", "journalist_id", "satellite"]}], "writes": [{"table": "wind_projects", "columns": ["transaction_date", "stateid", "journalist_id", "satellite"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tourist_attractions\", conn)\ndf.to_sql(\"athletes_performance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tourist_attractions", "columns": null}], "writes": [{"table": "athletes_performance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table policy_feedback --columns county_id,volunteer_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "policy_feedback", "columns": ["county_id", "volunteer_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT gamename, program_id FROM bi.refunds_daily LIMIT 383\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "bi.refunds_daily", "columns": ["gamename", "program_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO harvest_permits SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.bikes_available > 407).all()\n# src table: claims\nengine.execute(\"INSERT INTO chemical_processes SELECT * FROM claims\")\n", "labels": {"reads": [{"table": "claims", "columns": null}], "writes": [{"table": "chemical_processes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT carbon_footprint, fund_type FROM co2_sequestration LIMIT 388;\nINSERT INTO biotech.startups SELECT initiative_type, official_native_language, home_team_id FROM genetics.crispr WHERE initiative_type > 105;\nSQL\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": ["carbon_footprint", "fund_type"]}, {"table": "genetics.crispr", "columns": ["initiative_type", "official_native_language", "home_team_id"]}], "writes": [{"table": "biotech.startups", "columns": ["initiative_type", "official_native_language", "home_team_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO trainings SELECT policyholder_id, artwork, prominence, high_estimate FROM skincare_sales WHERE policyholder_id > 72\"\n", "labels": {"reads": [{"table": "skincare_sales", "columns": ["policyholder_id", "artwork", "prominence", "high_estimate"]}], "writes": [{"table": "trainings", "columns": ["policyholder_id", "artwork", "prominence", "high_estimate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"donorprograms\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "donorprograms", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO military_equipment_maintenance SELECT a.sellingprice, b.farmer_name FROM contracts a JOIN ods.clicks_full b ON a.address_line_2 = b.address_line_2\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "contracts", "columns": null}, {"table": "ods.clicks_full", "columns": null}], "writes": [{"table": "military_equipment_maintenance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"local_impact_japan\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "local_impact_japan", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"aircraft\")\nsrc.write.insertInto(\"dws.dws_inventory_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "aircraft", "columns": null}], "writes": [{"table": "dws.dws_inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO visitordemographics SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"film\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ref_colors\")\n", "labels": {"reads": [{"table": "film", "columns": null}], "writes": [{"table": "ref_colors", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO mart.mart_campaigns_daily SELECT year_working, cows, program_name FROM ads.ads_users_hourly WHERE year_working > 71\"\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": ["year_working", "cows", "program_name"]}], "writes": [{"table": "mart.mart_campaigns_daily", "columns": ["year_working", "cows", "program_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO inst (workforce_development, catalog_level_number) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "inst", "columns": ["workforce_development", "catalog_level_number"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO plankton SELECT street_address, skill_description, fare_date FROM dw_payments WHERE street_address > 83\"], check=True)\n", "labels": {"reads": [{"table": "dw_payments", "columns": ["street_address", "skill_description", "fare_date"]}], "writes": [{"table": "plankton", "columns": ["street_address", "skill_description", "fare_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO geologicalsurvey (region_code, next_entry_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "geologicalsurvey", "columns": ["region_code", "next_entry_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM yoga\"\n", "labels": {"reads": [{"table": "yoga", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"construction_labor\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mining_operation\")\n", "labels": {"reads": [{"table": "construction_labor", "columns": null}], "writes": [{"table": "mining_operation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sports\", conn)\ndf.to_sql(\"steps\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sports", "columns": null}], "writes": [{"table": "steps", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"gradeconversion\")\npush_to_store(df, \"causes_insert_2\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gradeconversion", "columns": null}], "writes": [{"table": "causes_insert_2", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO satellites_in_orbit SELECT a.contractorid, b.matchdate FROM charging_stations a JOIN employment b ON a.vessel = b.vessel\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "charging_stations", "columns": null}, {"table": "employment", "columns": null}], "writes": [{"table": "satellites_in_orbit", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"textileworkers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "textileworkers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"patient_outcomes\")\nsrc.write.insertInto(\"customer_policies\", overwrite=True)\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": null}], "writes": [{"table": "customer_policies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table intelligence_agents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO intelligence_agency SELECT hourlyrate, visitor_country, lender_id, labor_hour_id FROM course_authors_and_tutors WHERE hourlyrate > 411\"\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": ["hourlyrate", "visitor_country", "lender_id", "labor_hour_id"]}], "writes": [{"table": "intelligence_agency", "columns": ["hourlyrate", "visitor_country", "lender_id", "labor_hour_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model diversity depends on viewership\ndbt build -s diversity --vars 'source: viewership'\n", "labels": {"reads": [{"table": "viewership", "columns": null}], "writes": [{"table": "diversity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model invoice depends on global_sales_2022\ndbt build --select invoice --vars '{\"src\":\"global_sales_2022\"}'\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}], "writes": [{"table": "invoice", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO communityhealthworkers SELECT * FROM legacy\ncur.execute(\"SELECT driverid, hardware_model_name FROM tours LIMIT 100\")\n", "labels": {"reads": [{"table": "tours", "columns": ["driverid", "hardware_model_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO trees SELECT unavailable, theftdate FROM caribbean_tourists WHERE unavailable > 146\"\n", "labels": {"reads": [{"table": "caribbean_tourists", "columns": ["unavailable", "theftdate"]}], "writes": [{"table": "trees", "columns": ["unavailable", "theftdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO cargo_data SELECT a.apid, b.topic FROM dws.coupon_use_di a JOIN investment_strategies b ON a.style = b.style\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dws.coupon_use_di", "columns": null}, {"table": "investment_strategies", "columns": null}], "writes": [{"table": "cargo_data", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.route_short_name > 162).all()\n# src table: waterusage\nengine.execute(\"INSERT INTO bi.payments_daily SELECT * FROM waterusage\")\n", "labels": {"reads": [{"table": "waterusage", "columns": null}], "writes": [{"table": "bi.payments_daily", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.device_log_hourly SELECT well_depth, engagement_date, ocean FROM multimodalhubs WHERE well_depth > 315\"\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": ["well_depth", "engagement_date", "ocean"]}], "writes": [{"table": "bi.device_log_hourly", "columns": ["well_depth", "engagement_date", "ocean"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM causes\"\n", "labels": {"reads": [{"table": "causes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO smartcities SELECT author_community, game_genre FROM africa_schema.african_mines WHERE author_community > 430\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["author_community", "game_genre"]}], "writes": [{"table": "smartcities", "columns": ["author_community", "game_genre"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg_payments_hourly SELECT transaction_type, port, count, online_dispute_resolution FROM eventattendance WHERE transaction_type > 396\"], check=True)\n", "labels": {"reads": [{"table": "eventattendance", "columns": ["transaction_type", "port", "count", "online_dispute_resolution"]}], "writes": [{"table": "stg_payments_hourly", "columns": ["transaction_type", "port", "count", "online_dispute_resolution"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.stg_exposure_daily --columns animal_type,head_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_exposure_daily", "columns": ["animal_type", "head_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO artworks SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"philadelphia_police_emergencies\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"train_lines\")\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": [{"table": "train_lines", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO tours SELECT a.organizationname, b.average FROM mine_workforce a JOIN products b ON a.emp_jobcode = b.emp_jobcode\"\n", "labels": {"reads": [{"table": "mine_workforce", "columns": null}, {"table": "products", "columns": null}], "writes": [{"table": "tours", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM construction_labor_stats\", conn)\ndf.to_sql(\"transportation_per_country\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": null}], "writes": [{"table": "transportation_per_country", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO invoice SELECT num_projects, scientific_name, farm_id, venue FROM navalequipmentmaintenance WHERE num_projects > 117\"\n", "labels": {"reads": [{"table": "navalequipmentmaintenance", "columns": ["num_projects", "scientific_name", "farm_id", "venue"]}], "writes": [{"table": "invoice", "columns": ["num_projects", "scientific_name", "farm_id", "venue"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO properties SELECT campusfee, plant_location FROM ads.ads_clicks_delta WHERE campusfee > 322\"\n", "labels": {"reads": [{"table": "ads.ads_clicks_delta", "columns": ["campusfee", "plant_location"]}], "writes": [{"table": "properties", "columns": ["campusfee", "plant_location"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO carbon_sequestration SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM products_booked\", conn)\ndf.to_sql(\"city_budgets\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "products_booked", "columns": null}], "writes": [{"table": "city_budgets", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO factories_africa SELECT industry, vehicle_details FROM soccer_goals WHERE industry > 20\")\n", "labels": {"reads": [{"table": "soccer_goals", "columns": ["industry", "vehicle_details"]}], "writes": [{"table": "factories_africa", "columns": ["industry", "vehicle_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table renewable_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT song_name, violationid FROM host\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"community_centers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "host", "columns": ["song_name", "violationid"]}], "writes": [{"table": "community_centers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"genre_songs\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"draft_copies\")\n", "labels": {"reads": [{"table": "genre_songs", "columns": null}], "writes": [{"table": "draft_copies", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dw.inventory_delta\"\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"carbon_offset_programs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table patient_satisfaction --columns extractiondate,media_type_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "patient_satisfaction", "columns": ["extractiondate", "media_type_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT famous_title, instructor_id FROM locations LIMIT 386;\nINSERT INTO airport SELECT dish_name, excavationid, practice FROM garmentproduction WHERE dish_name > 342;\nSQL\n", "labels": {"reads": [{"table": "locations", "columns": ["famous_title", "instructor_id"]}, {"table": "garmentproduction", "columns": ["dish_name", "excavationid", "practice"]}], "writes": [{"table": "airport", "columns": ["dish_name", "excavationid", "practice"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM haircare_cruelty\", conn)\ndf.to_sql(\"equipment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}], "writes": [{"table": "equipment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO broadband_customers_global SELECT date_left_staff, playername, market_rate FROM daily_oil_production WHERE date_left_staff > 270\"], check=True)\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": ["date_left_staff", "playername", "market_rate"]}], "writes": [{"table": "broadband_customers_global", "columns": ["date_left_staff", "playername", "market_rate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"strandings\")\nsrc.write.insertInto(\"habitat\", overwrite=True)\n", "labels": {"reads": [{"table": "strandings", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"ship_agent\")\nexport_to_store(df, \"economic_diversification\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ship_agent", "columns": null}], "writes": [{"table": "economic_diversification", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model fish_suppliers depends on attendees\ndbt run --select fish_suppliers --vars 'source: attendees'\n", "labels": {"reads": [{"table": "attendees", "columns": null}], "writes": [{"table": "fish_suppliers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO ads.ads_vendors_hourly SELECT a.yearadded, b.contributiondate FROM rural_infrastructure a JOIN elimination b ON a.ironid = b.ironid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rural_infrastructure", "columns": null}, {"table": "elimination", "columns": null}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"production_yearly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dwd.dwd_payments_di\")\n", "labels": {"reads": [{"table": "production_yearly", "columns": null}], "writes": [{"table": "dwd.dwd_payments_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"happy_hour\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"algorithmic_fairness\")\n", "labels": {"reads": [{"table": "happy_hour", "columns": null}], "writes": [{"table": "algorithmic_fairness", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO size (trial_name, lesson_status_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "size", "columns": ["trial_name", "lesson_status_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bookings SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT home_team_points, fault_status FROM co2_emission_reduction\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"shop\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "co2_emission_reduction", "columns": ["home_team_points", "fault_status"]}], "writes": [{"table": "shop", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"flight_emissions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "flight_emissions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"levees\").toPandas()\ndf[[\"decoration_theme\", \"organizationid\"]].to_sql(\"ancient_cultures\", engine, index=False)\n", "labels": {"reads": [{"table": "levees", "columns": null}], "writes": [{"table": "ancient_cultures", "columns": ["decoration_theme", "organizationid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.facid > 316).all()\n# src table: dw.shipments_di\nengine.execute(\"INSERT INTO ref_budget_codes SELECT * FROM dw.shipments_di\")\n", "labels": {"reads": [{"table": "dw.shipments_di", "columns": null}], "writes": [{"table": "ref_budget_codes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_exposure_delta\").toPandas()\ndf[[\"court_appearances\", \"player\"]].to_sql(\"enrolled_in\", engine, index=False)\n", "labels": {"reads": [{"table": "ods_exposure_delta", "columns": null}], "writes": [{"table": "enrolled_in", "columns": ["court_appearances", "player"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT share_in_percent, personnelid FROM size\", engine)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"art_pieces\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "size", "columns": ["share_in_percent", "personnelid"]}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO contract_states SELECT attendance_id, worker_name, num_shariah_compliant_investments, image_name FROM campaigns WHERE attendance_id > 420\"\n", "labels": {"reads": [{"table": "campaigns", "columns": ["attendance_id", "worker_name", "num_shariah_compliant_investments", "image_name"]}], "writes": [{"table": "contract_states", "columns": ["attendance_id", "worker_name", "num_shariah_compliant_investments", "image_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO regulatory_compliance SELECT satelliteid, alert_id, theatrename FROM impact_investments WHERE satelliteid > 217\"\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["satelliteid", "alert_id", "theatrename"]}], "writes": [{"table": "regulatory_compliance", "columns": ["satelliteid", "alert_id", "theatrename"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT employee_address_id, feedid FROM mentalhealthprovider LIMIT 138\")\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO student_access SELECT operationid, studentid, reviews, art_type FROM inventory WHERE operationid > 208\")\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": ["employee_address_id", "feedid"]}, {"table": "inventory", "columns": ["operationid", "studentid", "reviews", "art_type"]}], "writes": [{"table": "student_access", "columns": ["operationid", "studentid", "reviews", "art_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"foodsafetyrecords\").toPandas()\ndf[[\"ticketprice\", \"frequency\"]].to_sql(\"farmers\", engine, index=False)\n", "labels": {"reads": [{"table": "foodsafetyrecords", "columns": null}], "writes": [{"table": "farmers", "columns": ["ticketprice", "frequency"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO casebilling SELECT title, maintenance_id, vaccinations, reason FROM company WHERE title > 158\"\n", "labels": {"reads": [{"table": "company", "columns": ["title", "maintenance_id", "vaccinations", "reason"]}], "writes": [{"table": "casebilling", "columns": ["title", "maintenance_id", "vaccinations", "reason"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO research_staff SELECT sector_id, coverage_type FROM ods.vendors_di WHERE sector_id > 37\"\n", "labels": {"reads": [{"table": "ods.vendors_di", "columns": ["sector_id", "coverage_type"]}], "writes": [{"table": "research_staff", "columns": ["sector_id", "coverage_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.sessions_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO support_programs (activity_date, biz_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "support_programs", "columns": ["activity_date", "biz_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT customer_id, pettype FROM renewable_projects\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"ods.ods_device_log_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "renewable_projects", "columns": ["customer_id", "pettype"]}], "writes": [{"table": "ods.ods_device_log_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM healthcare_budget\", conn)\ndf.to_sql(\"county_public_safety\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": [{"table": "county_public_safety", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"funding_rounds\")\nupsert_to_store(df, \"climate_communication\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "funding_rounds", "columns": null}], "writes": [{"table": "climate_communication", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"disease_prevalence\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"farmers\")\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": [{"table": "farmers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ethicalaibudget SELECT country_id, moisture_level, city_name FROM communityhealthworkerscanada WHERE country_id > 43\"\n", "labels": {"reads": [{"table": "communityhealthworkerscanada", "columns": ["country_id", "moisture_level", "city_name"]}], "writes": [{"table": "ethicalaibudget", "columns": ["country_id", "moisture_level", "city_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model restaurants_tx depends on mart_refunds\ndbt run -s restaurants_tx --vars '{\"source_table\":\"mart_refunds\"}'\n", "labels": {"reads": [{"table": "mart_refunds", "columns": null}], "writes": [{"table": "restaurants_tx", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mars_spacecraft SELECT event, materialid FROM store_district WHERE event > 99\"\n", "labels": {"reads": [{"table": "store_district", "columns": ["event", "materialid"]}], "writes": [{"table": "mars_spacecraft", "columns": ["event", "materialid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT player_id, signupdate FROM attorney_billing_rates LIMIT 63;\nINSERT INTO mealtypes SELECT author_or_editor, purchases, draft_pick_number FROM humanitarian_assistance WHERE author_or_editor > 464;\nSQL\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": ["player_id", "signupdate"]}, {"table": "humanitarian_assistance", "columns": ["author_or_editor", "purchases", "draft_pick_number"]}], "writes": [{"table": "mealtypes", "columns": ["author_or_editor", "purchases", "draft_pick_number"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table accounts --columns code,occupancy_rate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "accounts", "columns": ["code", "occupancy_rate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT project_details, painting_name FROM ai_ethics_policies LIMIT 36\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO budget_allocations SELECT to_address, fda_approved, total_employees FROM territory.human_rights_data WHERE to_address > 349\")\n", "labels": {"reads": [{"table": "ai_ethics_policies", "columns": ["project_details", "painting_name"]}, {"table": "territory.human_rights_data", "columns": ["to_address", "fda_approved", "total_employees"]}], "writes": [{"table": "budget_allocations", "columns": ["to_address", "fda_approved", "total_employees"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM trucks\"\n", "labels": {"reads": [{"table": "trucks", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO stg.stg_users_di SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO catalog_contents_additional_attributes (sale_amount, trial_success_rate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "catalog_contents_additional_attributes", "columns": ["sale_amount", "trial_success_rate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO textile_sourcing SELECT a.end_time, b.customer_address_id FROM stg.stg_coupon_use_hourly a JOIN bi.clicks_hourly b ON a.dormid = b.dormid\"\n", "labels": {"reads": [{"table": "stg.stg_coupon_use_hourly", "columns": null}, {"table": "bi.clicks_hourly", "columns": null}], "writes": [{"table": "textile_sourcing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO fraud_detections (biomass, hire_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "fraud_detections", "columns": ["biomass", "hire_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"brand_info\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"engineer_visits\")\n", "labels": {"reads": [{"table": "brand_info", "columns": null}], "writes": [{"table": "engineer_visits", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bike_share\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "bike_share", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO donors_region SELECT * FROM legacy\ncur.execute(\"SELECT process_id, stu_hrs FROM retail_workers_union LIMIT 346\")\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": ["process_id", "stu_hrs"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO labour_productivity SELECT grant_id, farm_id FROM textile_waste WHERE grant_id > 213\")\n", "labels": {"reads": [{"table": "textile_waste", "columns": ["grant_id", "farm_id"]}], "writes": [{"table": "labour_productivity", "columns": ["grant_id", "farm_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table bi.bi_events_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dw_member_point_full SELECT date_of_latest_logon, request_id, lname, haslegalprecedent FROM prereq WHERE date_of_latest_logon > 59\"], check=True)\n", "labels": {"reads": [{"table": "prereq", "columns": ["date_of_latest_logon", "request_id", "lname", "haslegalprecedent"]}], "writes": [{"table": "dw_member_point_full", "columns": ["date_of_latest_logon", "request_id", "lname", "haslegalprecedent"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"habitat_preservation\")\nsrc.write.insertInto(\"southeast_providers\", overwrite=True)\n", "labels": {"reads": [{"table": "habitat_preservation", "columns": null}], "writes": [{"table": "southeast_providers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mars_missions\").toPandas()\ndf[[\"fare_amount\", \"attack_count\"]].to_sql(\"flu_shots\", engine, index=False)\n", "labels": {"reads": [{"table": "mars_missions", "columns": null}], "writes": [{"table": "flu_shots", "columns": ["fare_amount", "attack_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT resolved, parameters FROM mental_health_professionals_2 LIMIT 57;\nINSERT INTO nomination SELECT volunteer_id, treatment_date FROM cybersecurity_strategies WHERE volunteer_id > 359;\nSQL\n", "labels": {"reads": [{"table": "mental_health_professionals_2", "columns": ["resolved", "parameters"]}, {"table": "cybersecurity_strategies", "columns": ["volunteer_id", "treatment_date"]}], "writes": [{"table": "nomination", "columns": ["volunteer_id", "treatment_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO rural_clinics (city_id, middle_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "rural_clinics", "columns": ["city_id", "middle_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO nailpolishsales SELECT a.attack_count, b.hireyear FROM school_enrollment a JOIN bi.clicks_df b ON a.job_title = b.job_title\"\n", "labels": {"reads": [{"table": "school_enrollment", "columns": null}, {"table": "bi.clicks_df", "columns": null}], "writes": [{"table": "nailpolishsales", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT emissions, event_type FROM laborstatistics LIMIT 6;\nINSERT INTO ai_safety SELECT ethical_certifications, do_value FROM community_health_center WHERE ethical_certifications > 339;\nSQL\n", "labels": {"reads": [{"table": "laborstatistics", "columns": ["emissions", "event_type"]}, {"table": "community_health_center", "columns": ["ethical_certifications", "do_value"]}], "writes": [{"table": "ai_safety", "columns": ["ethical_certifications", "do_value"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM co2price\", conn)\ndf.to_sql(\"store\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "co2price", "columns": null}], "writes": [{"table": "store", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO country_renewable_energy SELECT user_rating, crop_name FROM mental_health_parity WHERE user_rating > 359\"\n", "labels": {"reads": [{"table": "mental_health_parity", "columns": ["user_rating", "crop_name"]}], "writes": [{"table": "country_renewable_energy", "columns": ["user_rating", "crop_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_campaigns_full\").toPandas()\ndf[[\"date_of_transaction\", \"sportname\"]].to_sql(\"judges\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": null}], "writes": [{"table": "judges", "columns": ["date_of_transaction", "sportname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO regional_archaeologists SELECT preferred_foot, patient_count, birth_date FROM water_conservation_brazil WHERE preferred_foot > 418\"\n", "labels": {"reads": [{"table": "water_conservation_brazil", "columns": ["preferred_foot", "patient_count", "birth_date"]}], "writes": [{"table": "regional_archaeologists", "columns": ["preferred_foot", "patient_count", "birth_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO branch (closuredate, preference_rating) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "branch", "columns": ["closuredate", "preference_rating"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_products_full\"\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM categories\"\n", "labels": {"reads": [{"table": "categories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO excavation SELECT date_incident_end, section_title, party_email FROM expenses WHERE date_incident_end > 45\")\n", "labels": {"reads": [{"table": "expenses", "columns": ["date_incident_end", "section_title", "party_email"]}], "writes": [{"table": "excavation", "columns": ["date_incident_end", "section_title", "party_email"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT quality, hosts FROM follows LIMIT 103;\nINSERT INTO satellites_in_orbit SELECT college_location, attack_count FROM hydro_power WHERE college_location > 236;\nSQL\n", "labels": {"reads": [{"table": "follows", "columns": ["quality", "hosts"]}, {"table": "hydro_power", "columns": ["college_location", "attack_count"]}], "writes": [{"table": "satellites_in_orbit", "columns": ["college_location", "attack_count"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model player depends on artifactanalysis\ndbt run --models player --vars '{\"source_table\":\"artifactanalysis\"}'\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}], "writes": [{"table": "player", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT equipment_id, roaming_country FROM experience LIMIT 206\")\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO regional_railways SELECT pname, budget_amount, discount FROM mental_health_parity WHERE pname > 25\")\n", "labels": {"reads": [{"table": "experience", "columns": ["equipment_id", "roaming_country"]}, {"table": "mental_health_parity", "columns": ["pname", "budget_amount", "discount"]}], "writes": [{"table": "regional_railways", "columns": ["pname", "budget_amount", "discount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model buildingpermits depends on housingaffordability\ndbt build --select buildingpermits --vars '{\"src\":\"housingaffordability\"}'\n", "labels": {"reads": [{"table": "housingaffordability", "columns": null}], "writes": [{"table": "buildingpermits", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT volunteerjoindate, daily_co2_emission FROM ref_colors LIMIT 117;\nINSERT INTO billstatus SELECT official_name, mar, tree_id FROM coral_reefs WHERE official_name > 269;\nSQL\n", "labels": {"reads": [{"table": "ref_colors", "columns": ["volunteerjoindate", "daily_co2_emission"]}, {"table": "coral_reefs", "columns": ["official_name", "mar", "tree_id"]}], "writes": [{"table": "billstatus", "columns": ["official_name", "mar", "tree_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"makeup_sales\")\nsrc.write.insertInto(\"volume\", overwrite=True)\n", "labels": {"reads": [{"table": "makeup_sales", "columns": null}], "writes": [{"table": "volume", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO fruitimport SELECT content_id, machine_series FROM donorprograms WHERE content_id > 444\"\n", "labels": {"reads": [{"table": "donorprograms", "columns": ["content_id", "machine_series"]}], "writes": [{"table": "fruitimport", "columns": ["content_id", "machine_series"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO reverselogisticstransactions SELECT quantity_containers, crop_type, mappinglength, views FROM dwd.exposure_hourly WHERE quantity_containers > 285\")\n", "labels": {"reads": [{"table": "dwd.exposure_hourly", "columns": ["quantity_containers", "crop_type", "mappinglength", "views"]}], "writes": [{"table": "reverselogisticstransactions", "columns": ["quantity_containers", "crop_type", "mappinglength", "views"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table conservation --target-dir /tmp/land\n", "labels": {"reads": [{"table": "conservation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.policy_number > 414).all()\n# src table: funding_rounds\nengine.execute(\"INSERT INTO safety_incidents_india SELECT * FROM funding_rounds\")\n", "labels": {"reads": [{"table": "funding_rounds", "columns": null}], "writes": [{"table": "safety_incidents_india", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model publicchargingstations depends on victims\ndbt build -s publicchargingstations --vars '{\"source_table\":\"victims\"}'\n", "labels": {"reads": [{"table": "victims", "columns": null}], "writes": [{"table": "publicchargingstations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.risk_score_hourly SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table gamestats --columns acidity,driller --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "gamestats", "columns": ["acidity", "driller"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"flight_emissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"cybersecurity_vulnerabilities\")\n", "labels": {"reads": [{"table": "flight_emissions", "columns": null}], "writes": [{"table": "cybersecurity_vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT continent_id, animal FROM perpetrator LIMIT 229\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO programs SELECT campaign, strategy_name FROM dw_member_point_full WHERE campaign > 247\")\n", "labels": {"reads": [{"table": "perpetrator", "columns": ["continent_id", "animal"]}, {"table": "dw_member_point_full", "columns": ["campaign", "strategy_name"]}], "writes": [{"table": "programs", "columns": ["campaign", "strategy_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO prison SELECT store_phone, grant_end_date, satellite FROM funding_records WHERE store_phone > 478\"\n", "labels": {"reads": [{"table": "funding_records", "columns": ["store_phone", "grant_end_date", "satellite"]}], "writes": [{"table": "prison", "columns": ["store_phone", "grant_end_date", "satellite"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wind_energy\").toPandas()\ndf[[\"improvement\", \"blockcode\"]].to_sql(\"total_capacity\", engine, index=False)\n", "labels": {"reads": [{"table": "wind_energy", "columns": null}], "writes": [{"table": "total_capacity", "columns": ["improvement", "blockcode"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT comments, subscribe_date FROM haircare_sales LIMIT 394;\nINSERT INTO permian_basin SELECT programid, energy_star_rating, passenger_id FROM workout WHERE programid > 88;\nSQL\n", "labels": {"reads": [{"table": "haircare_sales", "columns": ["comments", "subscribe_date"]}, {"table": "workout", "columns": ["programid", "energy_star_rating", "passenger_id"]}], "writes": [{"table": "permian_basin", "columns": ["programid", "energy_star_rating", "passenger_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO parking_fines SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO thefttypes SELECT surname, collection_id FROM upgrades WHERE surname > 500\"], check=True)\n", "labels": {"reads": [{"table": "upgrades", "columns": ["surname", "collection_id"]}], "writes": [{"table": "thefttypes", "columns": ["surname", "collection_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"landfill_capacity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"renewables.renewable_projects\")\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}], "writes": [{"table": "renewables.renewable_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO low_value_contracts SELECT a.annual_revenue, b.emissions FROM measurement a JOIN fault_log_parts b ON a.result = b.result\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "measurement", "columns": null}, {"table": "fault_log_parts", "columns": null}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT departmentid, coverage_type FROM marketingbudget LIMIT 148\")\nresult = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO user_profiles SELECT amountdonated, event_type FROM policyadvocacyevents WHERE amountdonated > 22\")\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["departmentid", "coverage_type"]}, {"table": "policyadvocacyevents", "columns": ["amountdonated", "event_type"]}], "writes": [{"table": "user_profiles", "columns": ["amountdonated", "event_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table multimodalhubs --columns facility_code,settlement_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "multimodalhubs", "columns": ["facility_code", "settlement_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"galleries\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "galleries", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO prepaid_mobile SELECT trackid, fleet_id, amenid FROM ocean_salinity WHERE trackid > 305\"\n", "labels": {"reads": [{"table": "ocean_salinity", "columns": ["trackid", "fleet_id", "amenid"]}], "writes": [{"table": "prepaid_mobile", "columns": ["trackid", "fleet_id", "amenid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"drought_impact\").toPandas()\ndf[[\"open_year\", \"market_value_billion\"]].to_sql(\"recovery_program\", engine, index=False)\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}], "writes": [{"table": "recovery_program", "columns": ["open_year", "market_value_billion"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO security_incidents SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table landfillcapacitybycountry --columns feature_details,join_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "landfillcapacitybycountry", "columns": ["feature_details", "join_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO green_buildings SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT organization, operating_system FROM authors\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"coralreefs\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "authors", "columns": ["organization", "operating_system"]}], "writes": [{"table": "coralreefs", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"shared_escooters\")\nsrc.write.insertInto(\"mart.risk_score_df\", overwrite=True)\n", "labels": {"reads": [{"table": "shared_escooters", "columns": null}], "writes": [{"table": "mart.risk_score_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_vendors\").toPandas()\ndf[[\"fueldate\", \"user_category\"]].to_sql(\"user_workouts_march\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_vendors", "columns": null}], "writes": [{"table": "user_workouts_march", "columns": ["fueldate", "user_category"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO chemical_production_5 (approval_date, form_type_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "chemical_production_5", "columns": ["approval_date", "form_type_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO hospitallocations SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO org_climate_finance SELECT quality_rank, visit_date, acc_type FROM average WHERE quality_rank > 257\"\n", "labels": {"reads": [{"table": "average", "columns": ["quality_rank", "visit_date", "acc_type"]}], "writes": [{"table": "org_climate_finance", "columns": ["quality_rank", "visit_date", "acc_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO brazil_projects SELECT src_apid, author, cmi_details FROM nutritionfacts WHERE src_apid > 98\"\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": ["src_apid", "author", "cmi_details"]}], "writes": [{"table": "brazil_projects", "columns": ["src_apid", "author", "cmi_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM green_building_projects\", conn)\ndf.to_sql(\"distributors\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "green_building_projects", "columns": null}], "writes": [{"table": "distributors", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tennis_players SELECT email_address, recycling_rate, date_from FROM grapes WHERE email_address > 428\"], check=True)\n", "labels": {"reads": [{"table": "grapes", "columns": ["email_address", "recycling_rate", "date_from"]}], "writes": [{"table": "tennis_players", "columns": ["email_address", "recycling_rate", "date_from"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sustainable_materials\", conn)\ndf.to_sql(\"veterans\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": null}], "writes": [{"table": "veterans", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"companies\")\nsrc.write.insertInto(\"weights\", overwrite=True)\n", "labels": {"reads": [{"table": "companies", "columns": null}], "writes": [{"table": "weights", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mammals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mammals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO player_attributes SELECT home_team_points, vesselname FROM programoutcomes WHERE home_team_points > 174\"], check=True)\n", "labels": {"reads": [{"table": "programoutcomes", "columns": ["home_team_points", "vesselname"]}], "writes": [{"table": "player_attributes", "columns": ["home_team_points", "vesselname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO investor SELECT strategy, machine_id, complaintid FROM scores WHERE strategy > 123\"\n", "labels": {"reads": [{"table": "scores", "columns": ["strategy", "machine_id", "complaintid"]}], "writes": [{"table": "investor", "columns": ["strategy", "machine_id", "complaintid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamegenres\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"certificate\")\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": [{"table": "certificate", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO seeds SELECT likes, last_workout_date, retweets FROM freightforwarding WHERE likes > 475\"\n", "labels": {"reads": [{"table": "freightforwarding", "columns": ["likes", "last_workout_date", "retweets"]}], "writes": [{"table": "seeds", "columns": ["likes", "last_workout_date", "retweets"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"landfill_capacity_city_v2\")\nsrc.write.insertInto(\"facility_production\", overwrite=True)\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": null}], "writes": [{"table": "facility_production", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO donors_region SELECT customer_country, leadershiptraining, implementation_year FROM water_conservation WHERE customer_country > 8\"\n", "labels": {"reads": [{"table": "water_conservation", "columns": ["customer_country", "leadershiptraining", "implementation_year"]}], "writes": [{"table": "donors_region", "columns": ["customer_country", "leadershiptraining", "implementation_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO mailshot_campaigns SELECT mhw_id, dept_store_chain_id, fault_description FROM factories_africa WHERE mhw_id > 231\")\n", "labels": {"reads": [{"table": "factories_africa", "columns": ["mhw_id", "dept_store_chain_id", "fault_description"]}], "writes": [{"table": "mailshot_campaigns", "columns": ["mhw_id", "dept_store_chain_id", "fault_description"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"district_schools\").toPandas()\ndf[[\"gamename\", \"max_temperature_f\"]].to_sql(\"ref_calendar\", engine, index=False)\n", "labels": {"reads": [{"table": "district_schools", "columns": null}], "writes": [{"table": "ref_calendar", "columns": ["gamename", "max_temperature_f"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wholesale_orders\").toPandas()\ndf[[\"surface_area\", \"area_size\"]].to_sql(\"auto_shows\", engine, index=False)\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": null}], "writes": [{"table": "auto_shows", "columns": ["surface_area", "area_size"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table classroom --columns produceid,stu_dob --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "classroom", "columns": ["produceid", "stu_dob"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mart.inventory_hourly\")\nsrc.write.insertInto(\"dws.dws_coupon_use_di\", overwrite=True)\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": null}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO item_inventory SELECT image_date, emergency_type, attribute_id, stu_num FROM spacemissions WHERE image_date > 157\"], check=True)\n", "labels": {"reads": [{"table": "spacemissions", "columns": ["image_date", "emergency_type", "attribute_id", "stu_num"]}], "writes": [{"table": "item_inventory", "columns": ["image_date", "emergency_type", "attribute_id", "stu_num"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"customer_events\")\nsrc.write.insertInto(\"trips\", overwrite=True)\n", "labels": {"reads": [{"table": "customer_events", "columns": null}], "writes": [{"table": "trips", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_tech\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "city_tech", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.refunds_hourly\"\n", "labels": {"reads": [{"table": "stg.refunds_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"business_rates\").toPandas()\ndf[[\"active_from_date\", \"plantid\"]].to_sql(\"game_scores\", engine, index=False)\n", "labels": {"reads": [{"table": "business_rates", "columns": null}], "writes": [{"table": "game_scores", "columns": ["active_from_date", "plantid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.years_operating > 9).all()\n# src table: solar_farms\nengine.execute(\"INSERT INTO editor SELECT * FROM solar_farms\")\n", "labels": {"reads": [{"table": "solar_farms", "columns": null}], "writes": [{"table": "editor", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO financial_capability_programs SELECT playergameid, sales_billion, grant_end_date, donor_state FROM mart.mart_refunds_di WHERE playergameid > 83;\nEOF\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": ["playergameid", "sales_billion", "grant_end_date", "donor_state"]}], "writes": [{"table": "financial_capability_programs", "columns": ["playergameid", "sales_billion", "grant_end_date", "donor_state"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"product_categories\")\nsrc.write.insertInto(\"ocean_acidification_antarctic\", overwrite=True)\n", "labels": {"reads": [{"table": "product_categories", "columns": null}], "writes": [{"table": "ocean_acidification_antarctic", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"patients\")\nsrc.write.insertInto(\"ods_risk_score_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "patients", "columns": null}], "writes": [{"table": "ods_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO open_pedagogy_exam SELECT * FROM legacy\ncur.execute(\"SELECT sport_id, ai_adoption FROM dws_coupon_use LIMIT 82\")\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["sport_id", "ai_adoption"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"port_visits\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"financial_capability_programs\")\n", "labels": {"reads": [{"table": "port_visits", "columns": null}], "writes": [{"table": "financial_capability_programs", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_formed, trade_name FROM marine_life_populations\", engine)\nmetrics.append(round(score, 4))\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"shops\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": ["date_formed", "trade_name"]}], "writes": [{"table": "shops", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"riskassessments\")\npush_to_warehouse(df, \"accessible_tech_categories\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "riskassessments", "columns": null}], "writes": [{"table": "accessible_tech_categories", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tech_workers_union\").toPandas()\ndf[[\"customer_details\", \"dish_type\"]].to_sql(\"habitats\", engine, index=False)\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}], "writes": [{"table": "habitats", "columns": ["customer_details", "dish_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"labor_hours\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"category_revenue\")\n", "labels": {"reads": [{"table": "labor_hours", "columns": null}], "writes": [{"table": "category_revenue", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO third_party_companies SELECT pages_per_minute_color, customer_status_code, trip_start_time, ai_model FROM wind_farms WHERE pages_per_minute_color > 45;\nEOF\n", "labels": {"reads": [{"table": "wind_farms", "columns": ["pages_per_minute_color", "customer_status_code", "trip_start_time", "ai_model"]}], "writes": [{"table": "third_party_companies", "columns": ["pages_per_minute_color", "customer_status_code", "trip_start_time", "ai_model"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"locations_oceania\")\nexport_to_store(df, \"counties\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "locations_oceania", "columns": null}], "writes": [{"table": "counties", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO contractnegotiations SELECT a.workout_id, b.trial_success_rate FROM immunization a JOIN spaceexploration b ON a.built_year = b.built_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "immunization", "columns": null}, {"table": "spaceexploration", "columns": null}], "writes": [{"table": "contractnegotiations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO haircaresales (requestid, tripid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "haircaresales", "columns": ["requestid", "tripid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"ods_vendors_daily\")\nsave_to_sink(df, \"stats\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods_vendors_daily", "columns": null}], "writes": [{"table": "stats", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO military_tech SELECT * FROM legacy\ncur.execute(\"SELECT stop, gas_fee FROM military_spending LIMIT 498\")\n", "labels": {"reads": [{"table": "military_spending", "columns": ["stop", "gas_fee"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd.dwd_products SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table water_sources --target-dir /tmp/land\n", "labels": {"reads": [{"table": "water_sources", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO satellite_missions_large SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table arctic_research --columns water_depth,vendor_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "arctic_research", "columns": ["water_depth", "vendor_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"organicproducts\")\nsrc.write.insertInto(\"developers\", overwrite=True)\n", "labels": {"reads": [{"table": "organicproducts", "columns": null}], "writes": [{"table": "developers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO movie SELECT manufacturerid, surname, virtual_tour_views, voter_id FROM experts WHERE manufacturerid > 127\"\n", "labels": {"reads": [{"table": "experts", "columns": ["manufacturerid", "surname", "virtual_tour_views", "voter_id"]}], "writes": [{"table": "movie", "columns": ["manufacturerid", "surname", "virtual_tour_views", "voter_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO diversification_projects SELECT contract_value, generation_date, cruelty_free FROM mart.device_log_hourly WHERE contract_value > 90\")\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": ["contract_value", "generation_date", "cruelty_free"]}], "writes": [{"table": "diversification_projects", "columns": ["contract_value", "generation_date", "cruelty_free"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO danceevents SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"department_store_chain\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"caribbeansea\")\n", "labels": {"reads": [{"table": "department_store_chain", "columns": null}], "writes": [{"table": "caribbeansea", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO world_heritage_sites SELECT a.team_id, b.last_year FROM evsales a JOIN workshops b ON a.claim_status_description = b.claim_status_description\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "evsales", "columns": null}, {"table": "workshops", "columns": null}], "writes": [{"table": "world_heritage_sites", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO professor SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cosmetics_sales (programarea, habitat_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cosmetics_sales", "columns": ["programarea", "habitat_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO units SELECT flight_number, donation_date, cargo_type FROM environmental_impact_stats WHERE flight_number > 378\"], check=True)\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": ["flight_number", "donation_date", "cargo_type"]}], "writes": [{"table": "units", "columns": ["flight_number", "donation_date", "cargo_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"sustainable_tourism_practices\")\nsrc.write.insertInto(\"bi_orders_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "sustainable_tourism_practices", "columns": null}], "writes": [{"table": "bi_orders_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO elimination SELECT esg_factor, center_name, deliveryid, characteristic_type_code FROM transport WHERE esg_factor > 50\"\n", "labels": {"reads": [{"table": "transport", "columns": ["esg_factor", "center_name", "deliveryid", "characteristic_type_code"]}], "writes": [{"table": "elimination", "columns": ["esg_factor", "center_name", "deliveryid", "characteristic_type_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO eu_humanitarian_assistance SELECT driller, transactions FROM ads.ads_campaigns_full WHERE driller > 293\"\n", "labels": {"reads": [{"table": "ads.ads_campaigns_full", "columns": ["driller", "transactions"]}], "writes": [{"table": "eu_humanitarian_assistance", "columns": ["driller", "transactions"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO open_pedagogy_enrollment (founder, contract_address) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "open_pedagogy_enrollment", "columns": ["founder", "contract_address"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO programs SELECT labor_id, centerid, fertilizer_id, avg_speed FROM algorithmic_fairness_incidents_monthly WHERE labor_id > 216\"\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["labor_id", "centerid", "fertilizer_id", "avg_speed"]}], "writes": [{"table": "programs", "columns": ["labor_id", "centerid", "fertilizer_id", "avg_speed"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"educationprograms\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"engineer_visits\")\n", "labels": {"reads": [{"table": "educationprograms", "columns": null}], "writes": [{"table": "engineer_visits", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mobile_plans SELECT outcome_name, artworkyear, student_capacity, bus_id FROM ads.ads_orders WHERE outcome_name > 396\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_orders", "columns": ["outcome_name", "artworkyear", "student_capacity", "bus_id"]}], "writes": [{"table": "mobile_plans", "columns": ["outcome_name", "artworkyear", "student_capacity", "bus_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"product_info\")\npersist_to_output(df, \"cultural_events\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "product_info", "columns": null}], "writes": [{"table": "cultural_events", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"disaster_response_donations\")\nsrc.write.insertInto(\"mart_exposure_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": null}], "writes": [{"table": "mart_exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO defenseprojects SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"arcticwildlifereserve\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"genetic.projects\")\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": null}], "writes": [{"table": "genetic.projects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO country_waste_generation SELECT country_name, party_id, personnel FROM caribbeansea WHERE country_name > 111\"\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["country_name", "party_id", "personnel"]}], "writes": [{"table": "country_waste_generation", "columns": ["country_name", "party_id", "personnel"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO winter_olympics SELECT year, invoice_number, day_of_week FROM mart.mart_member_point_hourly WHERE year > 16\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_member_point_hourly", "columns": ["year", "invoice_number", "day_of_week"]}], "writes": [{"table": "winter_olympics", "columns": ["year", "invoice_number", "day_of_week"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bi.bi_inventory_di SELECT license_type, event_count, sale_volume FROM locations WHERE license_type > 161\")\n", "labels": {"reads": [{"table": "locations", "columns": ["license_type", "event_count", "sale_volume"]}], "writes": [{"table": "bi.bi_inventory_di", "columns": ["license_type", "event_count", "sale_volume"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT museum, retailer_name FROM prison LIMIT 84\")\nmetrics.append(round(score, 4))\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO project_duration SELECT trip_duration, visitor_count, other_account_details FROM investor_activities WHERE trip_duration > 264\")\n", "labels": {"reads": [{"table": "prison", "columns": ["museum", "retailer_name"]}, {"table": "investor_activities", "columns": ["trip_duration", "visitor_count", "other_account_details"]}], "writes": [{"table": "project_duration", "columns": ["trip_duration", "visitor_count", "other_account_details"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.risk_score_daily (position, coverage_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.risk_score_daily", "columns": ["position", "coverage_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM imagery_archive\", conn)\ndf.to_sql(\"manager\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "imagery_archive", "columns": null}], "writes": [{"table": "manager", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO labor_practices SELECT moisture, numpieces, exhibitioncountry, initiative_region FROM public_works_projects WHERE moisture > 223\")\n", "labels": {"reads": [{"table": "public_works_projects", "columns": ["moisture", "numpieces", "exhibitioncountry", "initiative_region"]}], "writes": [{"table": "labor_practices", "columns": ["moisture", "numpieces", "exhibitioncountry", "initiative_region"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO public.trips_by_day_train SELECT a.song_id, b.restaurantid FROM arctic_sightings a JOIN drilling_rigs b ON a.spacecraft = b.spacecraft\"\n", "labels": {"reads": [{"table": "arctic_sightings", "columns": null}, {"table": "drilling_rigs", "columns": null}], "writes": [{"table": "public.trips_by_day_train", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO crime_incidents (mental_health_status, num_of_staff) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "crime_incidents", "columns": ["mental_health_status", "num_of_staff"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"catalog_contents\")\nsrc.write.insertInto(\"autoshows\", overwrite=True)\n", "labels": {"reads": [{"table": "catalog_contents", "columns": null}], "writes": [{"table": "autoshows", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM union_membership\"\n", "labels": {"reads": [{"table": "union_membership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads.ads_vendors_hourly (posted_at, grant_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_vendors_hourly", "columns": ["posted_at", "grant_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO apac_hotel_views SELECT characteristic_name, crop_id FROM vehicle_sales WHERE characteristic_name > 399\"], check=True)\n", "labels": {"reads": [{"table": "vehicle_sales", "columns": ["characteristic_name", "crop_id"]}], "writes": [{"table": "apac_hotel_views", "columns": ["characteristic_name", "crop_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO militaryinnovations (total_investment, vrdevice) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "militaryinnovations", "columns": ["total_investment", "vrdevice"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT offender_name, art_type FROM ads_sessions_di\", engine)\nlogger = logging.getLogger(__name__)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"workshops\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads_sessions_di", "columns": ["offender_name", "art_type"]}], "writes": [{"table": "workshops", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cargo_id, farmname FROM dws.dws_campaigns_df\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"on_call\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_campaigns_df", "columns": ["cargo_id", "farmname"]}], "writes": [{"table": "on_call", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"state_budget\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"user_activity\")\n", "labels": {"reads": [{"table": "state_budget", "columns": null}], "writes": [{"table": "user_activity", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO characteristics (created_date, saleid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "characteristics", "columns": ["created_date", "saleid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO phone_market SELECT professionalid, field_name, booking_end_date, patient_count FROM vehicledata WHERE professionalid > 418\"], check=True)\n", "labels": {"reads": [{"table": "vehicledata", "columns": ["professionalid", "field_name", "booking_end_date", "patient_count"]}], "writes": [{"table": "phone_market", "columns": ["professionalid", "field_name", "booking_end_date", "patient_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT retweets, university_type FROM green_projects LIMIT 269\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "green_projects", "columns": ["retweets", "university_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO club SELECT a.heartrate, b.maintenance_contract_id FROM fabricinventory a JOIN crime_stats b ON a.dispensary_name = b.dispensary_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fabricinventory", "columns": null}, {"table": "crime_stats", "columns": null}], "writes": [{"table": "club", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO food_justice_contributors SELECT a.opname, b.showid FROM player_attributes a JOIN fireincidents b ON a.degrees = b.degrees\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "player_attributes", "columns": null}, {"table": "fireincidents", "columns": null}], "writes": [{"table": "food_justice_contributors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_pd_hours\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "teacher_pd_hours", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT ratingdate, releasedate FROM defense_contractors LIMIT 483;\nINSERT INTO food_safety_inspections SELECT vrgameid, reaction_time, employee_address_id FROM communityevents WHERE vrgameid > 143;\nSQL\n", "labels": {"reads": [{"table": "defense_contractors", "columns": ["ratingdate", "releasedate"]}, {"table": "communityevents", "columns": ["vrgameid", "reaction_time", "employee_address_id"]}], "writes": [{"table": "food_safety_inspections", "columns": ["vrgameid", "reaction_time", "employee_address_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"communities\").toPandas()\ndf[[\"document_status_description\", \"document_structure_description\"]].to_sql(\"institution\", engine, index=False)\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": [{"table": "institution", "columns": ["document_status_description", "document_structure_description"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT courtname, volume FROM studentaccommodations LIMIT 457;\nINSERT INTO mexico_regions SELECT fda_approved, ingredient_id, treatment FROM dws.dws_inventory_hourly WHERE fda_approved > 408;\nSQL\n", "labels": {"reads": [{"table": "studentaccommodations", "columns": ["courtname", "volume"]}, {"table": "dws.dws_inventory_hourly", "columns": ["fda_approved", "ingredient_id", "treatment"]}], "writes": [{"table": "mexico_regions", "columns": ["fda_approved", "ingredient_id", "treatment"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO article_views SELECT * FROM legacy\ncur.execute(\"SELECT destination_id, first_donation_date FROM state_budget LIMIT 106\")\n", "labels": {"reads": [{"table": "state_budget", "columns": ["destination_id", "first_donation_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM communityhealthworkerscanada\", conn)\ndf.to_sql(\"researchers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "communityhealthworkerscanada", "columns": null}], "writes": [{"table": "researchers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.high_temperature > 75).all()\n# src table: mexico_regions\nengine.execute(\"INSERT INTO public.developers SELECT * FROM mexico_regions\")\n", "labels": {"reads": [{"table": "mexico_regions", "columns": null}], "writes": [{"table": "public.developers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sid, black FROM has_allergy LIMIT 148;\nINSERT INTO maintenance_schedule SELECT biz_date, distance, defendant_id, petid FROM virtual_tours WHERE biz_date > 32;\nSQL\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["sid", "black"]}, {"table": "virtual_tours", "columns": ["biz_date", "distance", "defendant_id", "petid"]}], "writes": [{"table": "maintenance_schedule", "columns": ["biz_date", "distance", "defendant_id", "petid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"ods.ods_device_log_delta\")\nwrite_to_warehouse(df, \"doctors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": null}], "writes": [{"table": "doctors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO healthcare_centers SELECT u_id, effort, prereq_id FROM inspection WHERE u_id > 130;\nEOF\n", "labels": {"reads": [{"table": "inspection", "columns": ["u_id", "effort", "prereq_id"]}], "writes": [{"table": "healthcare_centers", "columns": ["u_id", "effort", "prereq_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"testtypes\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"smart_contracts_table\")\n", "labels": {"reads": [{"table": "testtypes", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"container_receipts\")\nsink_to_store(df, \"employeedemographics\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "container_receipts", "columns": null}], "writes": [{"table": "employeedemographics", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO atlantic_ocean_fish SELECT left_office, store_id FROM bank_info WHERE left_office > 61\")\n", "labels": {"reads": [{"table": "bank_info", "columns": ["left_office", "store_id"]}], "writes": [{"table": "atlantic_ocean_fish", "columns": ["left_office", "store_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"collective_bargaining\").toPandas()\ndf[[\"count_date\", \"amountdonated\"]].to_sql(\"bi_orders_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "collective_bargaining", "columns": null}], "writes": [{"table": "bi_orders_daily", "columns": ["count_date", "amountdonated"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO climate_investments SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO plays_games SELECT a.marketing_region_descriptrion, b.union_name FROM multimodalhubs a JOIN advisor b ON a.total_spent = b.total_spent\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": null}, {"table": "advisor", "columns": null}], "writes": [{"table": "plays_games", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO representative SELECT a.fair_trade, b.detection_date FROM community_development_projects a JOIN tennis_players b ON a.hoursspent = b.hoursspent\"\n", "labels": {"reads": [{"table": "community_development_projects", "columns": null}, {"table": "tennis_players", "columns": null}], "writes": [{"table": "representative", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO recycling_rates SELECT sensor_reading, address_content FROM ocean_species WHERE sensor_reading > 147;\nEOF\n", "labels": {"reads": [{"table": "ocean_species", "columns": ["sensor_reading", "address_content"]}], "writes": [{"table": "recycling_rates", "columns": ["sensor_reading", "address_content"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM therapy_session\"\n", "labels": {"reads": [{"table": "therapy_session", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model drama_workshop_groups depends on dws.dws_member_point_df\ndbt run --select drama_workshop_groups --vars '{\"src\":\"dws.dws_member_point_df\"}'\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": null}], "writes": [{"table": "drama_workshop_groups", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainableproduction SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO field SELECT trip_distance, active FROM ads.ads_products_full WHERE trip_distance > 436\"\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": ["trip_distance", "active"]}], "writes": [{"table": "field", "columns": ["trip_distance", "active"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT launch_date, friend FROM submersible_dives LIMIT 28;\nINSERT INTO pollutionincidents SELECT is_ev, num_fans, partnership_id, totaldonation FROM mineral_extraction WHERE is_ev > 448;\nSQL\n", "labels": {"reads": [{"table": "submersible_dives", "columns": ["launch_date", "friend"]}, {"table": "mineral_extraction", "columns": ["is_ev", "num_fans", "partnership_id", "totaldonation"]}], "writes": [{"table": "pollutionincidents", "columns": ["is_ev", "num_fans", "partnership_id", "totaldonation"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT time_month, draft_class FROM dw.dw_users_di LIMIT 456\")\nresult = value * ratio + offset\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO conservation_initiatives SELECT passenger_name, book_title FROM visits WHERE passenger_name > 459\")\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": ["time_month", "draft_class"]}, {"table": "visits", "columns": ["passenger_name", "book_title"]}], "writes": [{"table": "conservation_initiatives", "columns": ["passenger_name", "book_title"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO waste_management_projects SELECT 1\"\necho \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO electricvehiclestats SELECT eliminated_by, developer_id FROM platformh WHERE eliminated_by > 99\"\n", "labels": {"reads": [{"table": "platformh", "columns": ["eliminated_by", "developer_id"]}], "writes": [{"table": "electricvehiclestats", "columns": ["eliminated_by", "developer_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"freshwaterfinfish\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "freshwaterfinfish", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO emergencies SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table ngo_funding --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ngo_funding", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT workers, birth_place FROM paris_train\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"strandings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "paris_train", "columns": ["workers", "birth_place"]}], "writes": [{"table": "strandings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO product_info SELECT * FROM legacy\ncur.execute(\"SELECT ethical_manufacturing, vehicletype FROM web_client_accelerator LIMIT 193\")\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": ["ethical_manufacturing", "vehicletype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.sessions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"waste_generation_metrics\")\n", "labels": {"reads": [{"table": "dwd.sessions", "columns": null}], "writes": [{"table": "waste_generation_metrics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO show SELECT financial_capability_score, revenueid FROM energy_consumption WHERE financial_capability_score > 110\"], check=True)\n", "labels": {"reads": [{"table": "energy_consumption", "columns": ["financial_capability_score", "revenueid"]}], "writes": [{"table": "show", "columns": ["financial_capability_score", "revenueid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT booking_date, itemid FROM diversion_programs LIMIT 36;\nINSERT INTO haircaresales SELECT diversity_score, operation, grape, affected_population FROM election WHERE diversity_score > 428;\nSQL\n", "labels": {"reads": [{"table": "diversion_programs", "columns": ["booking_date", "itemid"]}, {"table": "election", "columns": ["diversity_score", "operation", "grape", "affected_population"]}], "writes": [{"table": "haircaresales", "columns": ["diversity_score", "operation", "grape", "affected_population"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model onlineengagement depends on patient_outcomes\ndbt run --models onlineengagement --vars '{\"source_table\":\"patient_outcomes\"}'\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": null}], "writes": [{"table": "onlineengagement", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cosmetics_sales --columns amount_claimed,dispensary --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cosmetics_sales", "columns": ["amount_claimed", "dispensary"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"participates_in\").toPandas()\ndf[[\"awayteamid\", \"tree_species\"]].to_sql(\"doctors\", engine, index=False)\n", "labels": {"reads": [{"table": "participates_in", "columns": null}], "writes": [{"table": "doctors", "columns": ["awayteamid", "tree_species"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"check_ins\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"research_vessels\")\n", "labels": {"reads": [{"table": "check_ins", "columns": null}], "writes": [{"table": "research_vessels", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cyber_incidents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "cyber_incidents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO film_market_estimation SELECT num_courses, certified, thefttypeid FROM teacher_pd WHERE num_courses > 147\"\n", "labels": {"reads": [{"table": "teacher_pd", "columns": ["num_courses", "certified", "thefttypeid"]}], "writes": [{"table": "film_market_estimation", "columns": ["num_courses", "certified", "thefttypeid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dorm_amenity SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO obesity SELECT a.spent, b.gas_fee FROM taj_mahal_visitors a JOIN models_safety b ON a.restaurant = b.restaurant\"\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": null}, {"table": "models_safety", "columns": null}], "writes": [{"table": "obesity", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO farms SELECT total_budget_percent_invested, unionid, total_amount, heartrate FROM ads_vendors_hourly WHERE total_budget_percent_invested > 93\")\n", "labels": {"reads": [{"table": "ads_vendors_hourly", "columns": ["total_budget_percent_invested", "unionid", "total_amount", "heartrate"]}], "writes": [{"table": "farms", "columns": ["total_budget_percent_invested", "unionid", "total_amount", "heartrate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"financial_capability_program\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.bi_risk_score_full\")\n", "labels": {"reads": [{"table": "financial_capability_program", "columns": null}], "writes": [{"table": "bi.bi_risk_score_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO mart.mart_member_point_hourly SELECT attorney_id, vaccine_type, form_type_code, college FROM public_works_projects WHERE attorney_id > 350\"\n", "labels": {"reads": [{"table": "public_works_projects", "columns": ["attorney_id", "vaccine_type", "form_type_code", "college"]}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": ["attorney_id", "vaccine_type", "form_type_code", "college"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT thefttype, model_name FROM esa_missions LIMIT 437;\nINSERT INTO employeedemographics SELECT gross_worldwide, impactid, parent_organization_id FROM communitycourtcases WHERE gross_worldwide > 317;\nSQL\n", "labels": {"reads": [{"table": "esa_missions", "columns": ["thefttype", "model_name"]}, {"table": "communitycourtcases", "columns": ["gross_worldwide", "impactid", "parent_organization_id"]}], "writes": [{"table": "employeedemographics", "columns": ["gross_worldwide", "impactid", "parent_organization_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO stg.stg_device_log_daily SELECT labor_hour_id, decor FROM cosmetics WHERE labor_hour_id > 194\"\n", "labels": {"reads": [{"table": "cosmetics", "columns": ["labor_hour_id", "decor"]}], "writes": [{"table": "stg.stg_device_log_daily", "columns": ["labor_hour_id", "decor"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.support_rate > 123).all()\n# src table: indie_artists\nengine.execute(\"INSERT INTO site SELECT * FROM indie_artists\")\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}], "writes": [{"table": "site", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"violations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.coupon_use\")\n", "labels": {"reads": [{"table": "violations", "columns": null}], "writes": [{"table": "bi.coupon_use", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table facility_production --columns operating_system,trial_status --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "facility_production", "columns": ["operating_system", "trial_status"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"landfill_capacity_city_v2\")\nsrc.write.insertInto(\"call_volume\", overwrite=True)\n", "labels": {"reads": [{"table": "landfill_capacity_city_v2", "columns": null}], "writes": [{"table": "call_volume", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"features\")\nsrc.write.insertInto(\"stg.stg_users_daily\", overwrite=True)\n", "labels": {"reads": [{"table": "features", "columns": null}], "writes": [{"table": "stg.stg_users_daily", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"militarydrones\").toPandas()\ndf[[\"market_value\", \"vehicle_details\"]].to_sql(\"follows\", engine, index=False)\n", "labels": {"reads": [{"table": "militarydrones", "columns": null}], "writes": [{"table": "follows", "columns": ["market_value", "vehicle_details"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ethical_ai (org_name, union_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ethical_ai", "columns": ["org_name", "union_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.garment_type > 370).all()\n# src table: ticketspending\nengine.execute(\"INSERT INTO hockey_players SELECT * FROM ticketspending\")\n", "labels": {"reads": [{"table": "ticketspending", "columns": null}], "writes": [{"table": "hockey_players", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT taskdate, customer_code FROM menu_categories LIMIT 424;\nINSERT INTO ref_service_types SELECT class_section, supplier, user_login FROM eventdates WHERE class_section > 36;\nSQL\n", "labels": {"reads": [{"table": "menu_categories", "columns": ["taskdate", "customer_code"]}, {"table": "eventdates", "columns": ["class_section", "supplier", "user_login"]}], "writes": [{"table": "ref_service_types", "columns": ["class_section", "supplier", "user_login"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model nba_games depends on bay_area_properties\ndbt run --select nba_games --vars '{\"src\":\"bay_area_properties\"}'\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": null}], "writes": [{"table": "nba_games", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT statename, funding FROM watertreatmentplants LIMIT 140;\nINSERT INTO editor SELECT individual_name, played, result FROM well_production WHERE individual_name > 330;\nSQL\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["statename", "funding"]}, {"table": "well_production", "columns": ["individual_name", "played", "result"]}], "writes": [{"table": "editor", "columns": ["individual_name", "played", "result"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"disaster_response_donations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"environmental_impact_stats\")\n", "labels": {"reads": [{"table": "disaster_response_donations", "columns": null}], "writes": [{"table": "environmental_impact_stats", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO community_development.transactions SELECT farmid, wrestler_id, doctorsper1000, away_team_three_point FROM genre_songs WHERE farmid > 85\"\n", "labels": {"reads": [{"table": "genre_songs", "columns": ["farmid", "wrestler_id", "doctorsper1000", "away_team_three_point"]}], "writes": [{"table": "community_development.transactions", "columns": ["farmid", "wrestler_id", "doctorsper1000", "away_team_three_point"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ai_model, calories FROM therapy_attendance\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"marketing_regions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": ["ai_model", "calories"]}], "writes": [{"table": "marketing_regions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO wastewater_treatment_plants SELECT vehicle_model, galleryname FROM medical_professionals WHERE vehicle_model > 415\"\n", "labels": {"reads": [{"table": "medical_professionals", "columns": ["vehicle_model", "galleryname"]}], "writes": [{"table": "wastewater_treatment_plants", "columns": ["vehicle_model", "galleryname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table smartcitytech --columns sessionid,wellname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "smartcitytech", "columns": ["sessionid", "wellname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO urban_transportation SELECT policy, policytype, diet, team_name FROM jupiter_missions WHERE policy > 58\"\n", "labels": {"reads": [{"table": "jupiter_missions", "columns": ["policy", "policytype", "diet", "team_name"]}], "writes": [{"table": "urban_transportation", "columns": ["policy", "policytype", "diet", "team_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT target_id, host_city FROM bi.device_log LIMIT 337\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO taxi_data SELECT effort_id, species_name FROM member_of WHERE effort_id > 444\")\n", "labels": {"reads": [{"table": "bi.device_log", "columns": ["target_id", "host_city"]}, {"table": "member_of", "columns": ["effort_id", "species_name"]}], "writes": [{"table": "taxi_data", "columns": ["effort_id", "species_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO habitat_preservation SELECT a.artist_gender, b.cargo_weight FROM temperature_data a JOIN bi_shipments_daily b ON a.num_employees = b.num_employees\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "temperature_data", "columns": null}, {"table": "bi_shipments_daily", "columns": null}], "writes": [{"table": "habitat_preservation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model donations depends on dws.events\ndbt build --select donations --vars '{\"src\":\"dws.events\"}'\n", "labels": {"reads": [{"table": "dws.events", "columns": null}], "writes": [{"table": "donations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT pets_allowed_yn, attribute_id FROM wildlife_sanctuaries LIMIT 301;\nINSERT INTO item_inventory SELECT playerregion, policy_count, import_country FROM grant WHERE playerregion > 1;\nSQL\n", "labels": {"reads": [{"table": "wildlife_sanctuaries", "columns": ["pets_allowed_yn", "attribute_id"]}, {"table": "grant", "columns": ["playerregion", "policy_count", "import_country"]}], "writes": [{"table": "item_inventory", "columns": ["playerregion", "policy_count", "import_country"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO restaurant SELECT launch_agency, item FROM marketingbudget WHERE launch_agency > 224\")\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["launch_agency", "item"]}], "writes": [{"table": "restaurant", "columns": ["launch_agency", "item"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT use_date, trade_name FROM mart_refunds LIMIT 455\")\nimport logging\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO specieswatertemp SELECT sale_volume, chargeable_amount, treatment_date, investors FROM mart.mart_products_df WHERE sale_volume > 195\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["use_date", "trade_name"]}, {"table": "mart.mart_products_df", "columns": ["sale_volume", "chargeable_amount", "treatment_date", "investors"]}], "writes": [{"table": "specieswatertemp", "columns": ["sale_volume", "chargeable_amount", "treatment_date", "investors"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_acidification\").toPandas()\ndf[[\"time_of_day\", \"site_id\"]].to_sql(\"economic_diversification\", engine, index=False)\n", "labels": {"reads": [{"table": "ocean_acidification", "columns": null}], "writes": [{"table": "economic_diversification", "columns": ["time_of_day", "site_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO programoutcomes SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO preferences SELECT * FROM legacy\ncur.execute(\"SELECT last_workout_date, ai_powered_features FROM security_incidents LIMIT 88\")\n", "labels": {"reads": [{"table": "security_incidents", "columns": ["last_workout_date", "ai_powered_features"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd.dwd_cart_item_di SELECT donationdate, team_id_br, company_name FROM arctic_weather WHERE donationdate > 331\"\n", "labels": {"reads": [{"table": "arctic_weather", "columns": ["donationdate", "team_id_br", "company_name"]}], "writes": [{"table": "dwd.dwd_cart_item_di", "columns": ["donationdate", "team_id_br", "company_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.users_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ods.ods_risk_score_full\")\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": [{"table": "ods.ods_risk_score_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT cargo, policyholderid FROM race LIMIT 14;\nINSERT INTO civilcases SELECT follow_up_date, ingredient_id, is_autonomous FROM participation WHERE follow_up_date > 63;\nSQL\n", "labels": {"reads": [{"table": "race", "columns": ["cargo", "policyholderid"]}, {"table": "participation", "columns": ["follow_up_date", "ingredient_id", "is_autonomous"]}], "writes": [{"table": "civilcases", "columns": ["follow_up_date", "ingredient_id", "is_autonomous"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO instructor SELECT a.star_rating_description, b.funding_received FROM certificate a JOIN aus_wellbeing b ON a.founder_identifies_as_lgbtq = b.founder_identifies_as_lgbtq\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "certificate", "columns": null}, {"table": "aus_wellbeing", "columns": null}], "writes": [{"table": "instructor", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"site\")\nsrc.write.insertInto(\"light_rail_lines\", overwrite=True)\n", "labels": {"reads": [{"table": "site", "columns": null}], "writes": [{"table": "light_rail_lines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM language\"\n", "labels": {"reads": [{"table": "language", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO military_contracts SELECT inventoryid, employee, donor_program FROM baseball_teams WHERE inventoryid > 401\"\n", "labels": {"reads": [{"table": "baseball_teams", "columns": ["inventoryid", "employee", "donor_program"]}], "writes": [{"table": "military_contracts", "columns": ["inventoryid", "employee", "donor_program"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM check_ins\", conn)\ndf.to_sql(\"dw_payments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "check_ins", "columns": null}], "writes": [{"table": "dw_payments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO precipitation_data SELECT visit_month, founding_year FROM licenses WHERE visit_month > 357\"\n", "labels": {"reads": [{"table": "licenses", "columns": ["visit_month", "founding_year"]}], "writes": [{"table": "precipitation_data", "columns": ["visit_month", "founding_year"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tweets SELECT game_id, blockfloor, staff_details FROM dancefunding WHERE game_id > 176\"\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["game_id", "blockfloor", "staff_details"]}], "writes": [{"table": "tweets", "columns": ["game_id", "blockfloor", "staff_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"police_officers_tx\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.vendors_full\")\n", "labels": {"reads": [{"table": "police_officers_tx", "columns": null}], "writes": [{"table": "mart.vendors_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM medical_professionals\"\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO virtual_tour_offers (business_id, document_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "virtual_tour_offers", "columns": ["business_id", "document_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"seamounts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"activities\")\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": [{"table": "activities", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO maintenance_engineers (creator, dock_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "maintenance_engineers", "columns": ["creator", "dock_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table environmental_impact_stats --columns tank,cargoid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "environmental_impact_stats", "columns": ["tank", "cargoid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT steps, green_building_id FROM production_rare_earth_elements LIMIT 413\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": ["steps", "green_building_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO playerscores SELECT artworkname, customer_first_name, airline FROM african_union_countries WHERE artworkname > 200\"\n", "labels": {"reads": [{"table": "african_union_countries", "columns": ["artworkname", "customer_first_name", "airline"]}], "writes": [{"table": "playerscores", "columns": ["artworkname", "customer_first_name", "airline"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ratings SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sports_events SELECT tripid, recycler_id, fabricid FROM green_projects WHERE tripid > 140\"\n", "labels": {"reads": [{"table": "green_projects", "columns": ["tripid", "recycler_id", "fabricid"]}], "writes": [{"table": "sports_events", "columns": ["tripid", "recycler_id", "fabricid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO models_safety SELECT a.store_id, b.base_name FROM workerbuildings a JOIN dwd.dwd_inventory_hourly b ON a.item_type = b.item_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "workerbuildings", "columns": null}, {"table": "dwd.dwd_inventory_hourly", "columns": null}], "writes": [{"table": "models_safety", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO spacecraftspeed (aid_name, daily_sales) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "spacecraftspeed", "columns": ["aid_name", "daily_sales"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"playergamedata\")\npersist_to_output(df, \"program_funding_2\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "playergamedata", "columns": null}], "writes": [{"table": "program_funding_2", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shipments_df depends on climate_data\ndbt run --select shipments_df --vars '{\"src\":\"climate_data\"}'\n", "labels": {"reads": [{"table": "climate_data", "columns": null}], "writes": [{"table": "shipments_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM contractnegotiations\", conn)\ndf.to_sql(\"school_enrollment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": null}], "writes": [{"table": "school_enrollment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM social_good_projects\", conn)\ndf.to_sql(\"vessel_incident_count\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "social_good_projects", "columns": null}], "writes": [{"table": "vessel_incident_count", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"audience_demographics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"southchinasea.wells\")\n", "labels": {"reads": [{"table": "audience_demographics", "columns": null}], "writes": [{"table": "southchinasea.wells", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT data_usage, detection_date FROM farmers_india LIMIT 409;\nINSERT INTO yoga SELECT exploited, deliveryid, next_maintenance FROM team_members WHERE exploited > 429;\nSQL\n", "labels": {"reads": [{"table": "farmers_india", "columns": ["data_usage", "detection_date"]}, {"table": "team_members", "columns": ["exploited", "deliveryid", "next_maintenance"]}], "writes": [{"table": "yoga", "columns": ["exploited", "deliveryid", "next_maintenance"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO flight_emissions SELECT floor_exercise_points, extraction_date FROM pilot_record WHERE floor_exercise_points > 214;\nEOF\n", "labels": {"reads": [{"table": "pilot_record", "columns": ["floor_exercise_points", "extraction_date"]}], "writes": [{"table": "flight_emissions", "columns": ["floor_exercise_points", "extraction_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"farm\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "farm", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"retailerg\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"red_line\")\n", "labels": {"reads": [{"table": "retailerg", "columns": null}], "writes": [{"table": "red_line", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO professor SELECT clientid, participant_type_code, theme FROM vehiclemodels WHERE clientid > 422;\nEOF\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": ["clientid", "participant_type_code", "theme"]}], "writes": [{"table": "professor", "columns": ["clientid", "participant_type_code", "theme"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO creative_ai (labor_cost, trial_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "creative_ai", "columns": ["labor_cost", "trial_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO student_program_mapping SELECT stu_num, visitor_country, acidity_level, suburb FROM drug_sales WHERE stu_num > 27\"\n", "labels": {"reads": [{"table": "drug_sales", "columns": ["stu_num", "visitor_country", "acidity_level", "suburb"]}], "writes": [{"table": "student_program_mapping", "columns": ["stu_num", "visitor_country", "acidity_level", "suburb"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT product_size, game_genre FROM excavations LIMIT 49\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "excavations", "columns": ["product_size", "game_genre"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cargo_handling SELECT development_type, medicine_id FROM west_providers WHERE development_type > 434\"\n", "labels": {"reads": [{"table": "west_providers", "columns": ["development_type", "medicine_id"]}], "writes": [{"table": "cargo_handling", "columns": ["development_type", "medicine_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_review\").toPandas()\ndf[[\"project_details\", \"artifact_name\"]].to_sql(\"community.donors\", engine, index=False)\n", "labels": {"reads": [{"table": "product_review", "columns": null}], "writes": [{"table": "community.donors", "columns": ["project_details", "artifact_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO pollution_initiatives SELECT inspection_date, booking_status_code, lname FROM model_data WHERE inspection_date > 428;\nEOF\n", "labels": {"reads": [{"table": "model_data", "columns": ["inspection_date", "booking_status_code", "lname"]}], "writes": [{"table": "pollution_initiatives", "columns": ["inspection_date", "booking_status_code", "lname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO arcticwildlifereserve SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO dailystreams SELECT taskdate, green_building_certified, material_date, technique_id FROM book WHERE taskdate > 463\"\n", "labels": {"reads": [{"table": "book", "columns": ["taskdate", "green_building_certified", "material_date", "technique_id"]}], "writes": [{"table": "dailystreams", "columns": ["taskdate", "green_building_certified", "material_date", "technique_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO concentrateprices SELECT production_rate, recycled, origin_city, vessel_name FROM yttrium_production WHERE production_rate > 15\"\n", "labels": {"reads": [{"table": "yttrium_production", "columns": ["production_rate", "recycled", "origin_city", "vessel_name"]}], "writes": [{"table": "concentrateprices", "columns": ["production_rate", "recycled", "origin_city", "vessel_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO swimmer SELECT airport_id, description FROM mart_refunds WHERE airport_id > 390\")\n", "labels": {"reads": [{"table": "mart_refunds", "columns": ["airport_id", "description"]}], "writes": [{"table": "swimmer", "columns": ["airport_id", "description"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table shariah_compliant_products --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shariah_compliant_products", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sponsor_name, safety_record FROM camera_lens\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"platform_production\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "camera_lens", "columns": ["sponsor_name", "safety_record"]}], "writes": [{"table": "platform_production", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM performers\"\n", "labels": {"reads": [{"table": "performers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO safetyorgs SELECT * FROM legacy\ncur.execute(\"SELECT city_population, genre_is FROM mental_health_professionals_2 LIMIT 492\")\n", "labels": {"reads": [{"table": "mental_health_professionals_2", "columns": ["city_population", "genre_is"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"travel_advisory\")\nsave_to_sink(df, \"satellites_in_orbit\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "satellites_in_orbit", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fish_biomass SELECT community_size, handling_id, fine, gender FROM dw.dw_coupon_use_daily WHERE community_size > 99\"\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": ["community_size", "handling_id", "fine", "gender"]}], "writes": [{"table": "fish_biomass", "columns": ["community_size", "handling_id", "fine", "gender"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO stg.stg_risk_score SELECT memory_in_g, habitat, baseprice, silver FROM languages WHERE memory_in_g > 411\"\n", "labels": {"reads": [{"table": "languages", "columns": ["memory_in_g", "habitat", "baseprice", "silver"]}], "writes": [{"table": "stg.stg_risk_score", "columns": ["memory_in_g", "habitat", "baseprice", "silver"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO threat_intelligence_budget SELECT a.case_outcome, b.video_id FROM laptimes a JOIN ads.ads_member_point_daily b ON a.stageposition = b.stageposition\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "laptimes", "columns": null}, {"table": "ads.ads_member_point_daily", "columns": null}], "writes": [{"table": "threat_intelligence_budget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT annual_carbon_offsets, clinic_name FROM discipline_enrollments LIMIT 340\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "discipline_enrollments", "columns": ["annual_carbon_offsets", "clinic_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO insurancetype (energy_star_rating, supplierid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "insurancetype", "columns": ["energy_star_rating", "supplierid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM new_schedules\", conn)\ndf.to_sql(\"project_issues\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "new_schedules", "columns": null}], "writes": [{"table": "project_issues", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table stg.risk_score_di --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM volunteer_registration\"\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT exhibit_location, savings FROM disabilityadvocacy LIMIT 297\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": ["exhibit_location", "savings"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO dws.payments_delta SELECT age_group_id, student_capacity FROM construction_union WHERE age_group_id > 25\")\n", "labels": {"reads": [{"table": "construction_union", "columns": ["age_group_id", "student_capacity"]}], "writes": [{"table": "dws.payments_delta", "columns": ["age_group_id", "student_capacity"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.wage > 342).all()\n# src table: esportsteamsafrica\nengine.execute(\"INSERT INTO facility_production SELECT * FROM esportsteamsafrica\")\n", "labels": {"reads": [{"table": "esportsteamsafrica", "columns": null}], "writes": [{"table": "facility_production", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_payments_full --columns num_of_staff,operating_system --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_payments_full", "columns": ["num_of_staff", "operating_system"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table route --columns machine_series,recipient_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "route", "columns": ["machine_series", "recipient_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"disaster_zones\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ocean\")\n", "labels": {"reads": [{"table": "disaster_zones", "columns": null}], "writes": [{"table": "ocean", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dish, app_name FROM ai_safety_incidents LIMIT 112\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ai_safety_incidents", "columns": ["dish", "app_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM user_genre\", conn)\ndf.to_sql(\"check_ins\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "user_genre", "columns": null}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stars\"\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM supply_chain\"\n", "labels": {"reads": [{"table": "supply_chain", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dwd.users_daily\")\nsrc.write.insertInto(\"race_ethnicity\", overwrite=True)\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": [{"table": "race_ethnicity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"military_technology_projects\")\nsrc.write.insertInto(\"customer_size_diversity\", overwrite=True)\n", "labels": {"reads": [{"table": "military_technology_projects", "columns": null}], "writes": [{"table": "customer_size_diversity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"manager_award\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"recycledmaterialsgarments\")\n", "labels": {"reads": [{"table": "manager_award", "columns": null}], "writes": [{"table": "recycledmaterialsgarments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"music_events\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "music_events", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.zone_id > 24).all()\n# src table: ads.users_full\nengine.execute(\"INSERT INTO item SELECT * FROM ads.users_full\")\n", "labels": {"reads": [{"table": "ads.users_full", "columns": null}], "writes": [{"table": "item", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.vendors_full\").toPandas()\ndf[[\"implementation_year\", \"hours_contributed\"]].to_sql(\"gardens\", engine, index=False)\n", "labels": {"reads": [{"table": "mart.vendors_full", "columns": null}], "writes": [{"table": "gardens", "columns": ["implementation_year", "hours_contributed"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO construction_union SELECT * FROM legacy\ncur.execute(\"SELECT building_short_name, pollution_id FROM offender_demographics LIMIT 39\")\n", "labels": {"reads": [{"table": "offender_demographics", "columns": ["building_short_name", "pollution_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO customer_transactions SELECT 1\"\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart_campaigns_delta\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"buildings\")\n", "labels": {"reads": [{"table": "mart_campaigns_delta", "columns": null}], "writes": [{"table": "buildings", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM material_production\", conn)\ndf.to_sql(\"ods.sessions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "material_production", "columns": null}], "writes": [{"table": "ods.sessions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mailshot_customers\"\n", "labels": {"reads": [{"table": "mailshot_customers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO goals SELECT a.program_name, b.clinic_name FROM menu_item a JOIN railway b ON a.fund_name = b.fund_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "menu_item", "columns": null}, {"table": "railway", "columns": null}], "writes": [{"table": "goals", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO healthcare SELECT * FROM legacy\ncur.execute(\"SELECT co2_emission, store_name FROM mobile_plans LIMIT 123\")\n", "labels": {"reads": [{"table": "mobile_plans", "columns": ["co2_emission", "store_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.dwd_device_log_delta (claim_id, race) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_device_log_delta", "columns": ["claim_id", "race"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO climatefinance SELECT capacity_mw, asset_name, invoice_details FROM property_community WHERE capacity_mw > 421;\nEOF\n", "labels": {"reads": [{"table": "property_community", "columns": ["capacity_mw", "asset_name", "invoice_details"]}], "writes": [{"table": "climatefinance", "columns": ["capacity_mw", "asset_name", "invoice_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO industry_funding SELECT student_id, friend FROM chip_model WHERE student_id > 65\"\n", "labels": {"reads": [{"table": "chip_model", "columns": ["student_id", "friend"]}], "writes": [{"table": "industry_funding", "columns": ["student_id", "friend"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.risk_score_df SELECT customer_country, channel FROM renewable_energy_projects WHERE customer_country > 78\"\n", "labels": {"reads": [{"table": "renewable_energy_projects", "columns": ["customer_country", "channel"]}], "writes": [{"table": "mart.risk_score_df", "columns": ["customer_country", "channel"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO album (fair_trade, fueldate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "album", "columns": ["fair_trade", "fueldate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model contract_transactions depends on mart_exposure_di\ndbt run --models contract_transactions --vars 'source: mart_exposure_di'\n", "labels": {"reads": [{"table": "mart_exposure_di", "columns": null}], "writes": [{"table": "contract_transactions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fabricinventory\"\n", "labels": {"reads": [{"table": "fabricinventory", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT floor_area_m2, ties FROM spacecraft_temperatures LIMIT 306;\nINSERT INTO cybersecurity_strategies SELECT artworkname, home_team, medical_professional_id FROM customer_size_diversity WHERE artworkname > 379;\nSQL\n", "labels": {"reads": [{"table": "spacecraft_temperatures", "columns": ["floor_area_m2", "ties"]}, {"table": "customer_size_diversity", "columns": ["artworkname", "home_team", "medical_professional_id"]}], "writes": [{"table": "cybersecurity_strategies", "columns": ["artworkname", "home_team", "medical_professional_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO roles SELECT a.trial_id, b.dock_count FROM music_streaming a JOIN temperaturehistory b ON a.asset_make = b.asset_make\"\n", "labels": {"reads": [{"table": "music_streaming", "columns": null}, {"table": "temperaturehistory", "columns": null}], "writes": [{"table": "roles", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT birth_date, weeks_on_top FROM ods_risk_score_delta LIMIT 258;\nINSERT INTO dwd.exposure_hourly SELECT sample_date, judge_state FROM inclusive_housing WHERE sample_date > 19;\nSQL\n", "labels": {"reads": [{"table": "ods_risk_score_delta", "columns": ["birth_date", "weeks_on_top"]}, {"table": "inclusive_housing", "columns": ["sample_date", "judge_state"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": ["sample_date", "judge_state"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitiondetails\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"product_reviews\")\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": null}], "writes": [{"table": "product_reviews", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO emergencyservices SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table hotel_revenue --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hotel_revenue", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_distribution\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"insurancetype\")\n", "labels": {"reads": [{"table": "water_distribution", "columns": null}], "writes": [{"table": "insurancetype", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cybersecurityincidents SELECT a.name_last, b.stateid FROM attendees a JOIN menu_item b ON a.role_description = b.role_description\"\n", "labels": {"reads": [{"table": "attendees", "columns": null}, {"table": "menu_item", "columns": null}], "writes": [{"table": "cybersecurityincidents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT formats, race FROM ai_projects\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"supportservices\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ai_projects", "columns": ["formats", "race"]}], "writes": [{"table": "supportservices", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.events_delta\", conn)\ndf.to_sql(\"dws.dws_shipments_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": null}], "writes": [{"table": "dws.dws_shipments_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT individual_last_name, actor_id FROM oceania_countries LIMIT 410\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [{"table": "oceania_countries", "columns": ["individual_last_name", "actor_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM worker_scores\"\n", "labels": {"reads": [{"table": "worker_scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model sanctuaryanimals depends on wins\ndbt run -s sanctuaryanimals --vars '{\"source_table\":\"wins\"}'\n", "labels": {"reads": [{"table": "wins", "columns": null}], "writes": [{"table": "sanctuaryanimals", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT waste_generation, outcome_id FROM militarycyberops LIMIT 157\")\nrows = cur.fetchall()\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "militarycyberops", "columns": ["waste_generation", "outcome_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table dws.cart_item_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM playergamedata\"\n", "labels": {"reads": [{"table": "playergamedata", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO waterconservationbudget SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM species_data\", conn)\ndf.to_sql(\"fishcaught\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "species_data", "columns": null}], "writes": [{"table": "fishcaught", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"brand_info\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"economic_diversification_efforts\")\n", "labels": {"reads": [{"table": "brand_info", "columns": null}], "writes": [{"table": "economic_diversification_efforts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO chemicalproducts SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dws.dws_coupon_use_di\"\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO labor_cost (retailer_name, screen_mode) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "labor_cost", "columns": ["retailer_name", "screen_mode"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT arrival, home_games FROM criminalcases LIMIT 33\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "criminalcases", "columns": ["arrival", "home_games"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO fish_farms SELECT organized_by, mental_health_rating FROM genetics_stats.research_projects WHERE organized_by > 436\")\n", "labels": {"reads": [{"table": "genetics_stats.research_projects", "columns": ["organized_by", "mental_health_rating"]}], "writes": [{"table": "fish_farms", "columns": ["organized_by", "mental_health_rating"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT productid, productlaunchdate FROM nursing_homes LIMIT 346;\nINSERT INTO chemical_production_5 SELECT quantitysold, assessment_date, membername FROM animals WHERE quantitysold > 374;\nSQL\n", "labels": {"reads": [{"table": "nursing_homes", "columns": ["productid", "productlaunchdate"]}, {"table": "animals", "columns": ["quantitysold", "assessment_date", "membername"]}], "writes": [{"table": "chemical_production_5", "columns": ["quantitysold", "assessment_date", "membername"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table evsales --columns customer_address,model_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "evsales", "columns": ["customer_address", "model_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT transaction_volume, itemid FROM ngo_funding LIMIT 24;\nINSERT INTO donationsbycause SELECT coowner_name, departure_date, document_code FROM intelligence_personnel WHERE coowner_name > 431;\nSQL\n", "labels": {"reads": [{"table": "ngo_funding", "columns": ["transaction_volume", "itemid"]}, {"table": "intelligence_personnel", "columns": ["coowner_name", "departure_date", "document_code"]}], "writes": [{"table": "donationsbycause", "columns": ["coowner_name", "departure_date", "document_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT status_of_thing_code, currency FROM bi.bi_payments_delta\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"drugs\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_payments_delta", "columns": ["status_of_thing_code", "currency"]}], "writes": [{"table": "drugs", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table rural_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table parking_fines --columns sales_amount,publish_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "parking_fines", "columns": ["sales_amount", "publish_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO storage SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_users_hourly\").toPandas()\ndf[[\"source_u_id\", \"cause\"]].to_sql(\"marine_species_indian\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": null}], "writes": [{"table": "marine_species_indian", "columns": ["source_u_id", "cause"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO recyclednylongarments SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT bioprocess_id, organic FROM player_stats LIMIT 7\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "player_stats", "columns": ["bioprocess_id", "organic"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stg.stg_products_delta (seal_species, home_city) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_products_delta", "columns": ["seal_species", "home_city"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ingredient_sourcing SELECT a.astronaut, b.average FROM ods_products_delta a JOIN team_members b ON a.schedule = b.schedule\"\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": null}, {"table": "team_members", "columns": null}], "writes": [{"table": "ingredient_sourcing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM candidate_assessments\", conn)\ndf.to_sql(\"manufacturingplants\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": null}], "writes": [{"table": "manufacturingplants", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wholesale_orders\").toPandas()\ndf[[\"mining_operation\", \"pname\"]].to_sql(\"tb_cases\", engine, index=False)\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": null}], "writes": [{"table": "tb_cases", "columns": ["mining_operation", "pname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model flight_safety depends on diversity\ndbt run -s flight_safety --vars 'source: diversity'\n", "labels": {"reads": [{"table": "diversity", "columns": null}], "writes": [{"table": "flight_safety", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO submarine_canyons SELECT first_year, tonnage FROM cargos WHERE first_year > 162\"\n", "labels": {"reads": [{"table": "cargos", "columns": ["first_year", "tonnage"]}], "writes": [{"table": "submarine_canyons", "columns": ["first_year", "tonnage"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO companies_extended SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO city.community_policing SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_products_hourly SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"campaigns_2023\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"list\")\n", "labels": {"reads": [{"table": "campaigns_2023", "columns": null}], "writes": [{"table": "list", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climatefinance\").toPandas()\ndf[[\"contract_start\", \"grantid\"]].to_sql(\"open_pedagogy\", engine, index=False)\n", "labels": {"reads": [{"table": "climatefinance", "columns": null}], "writes": [{"table": "open_pedagogy", "columns": ["contract_start", "grantid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dw.dw_risk_score_full --columns name_last,founding_location --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_risk_score_full", "columns": ["name_last", "founding_location"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO artifacts SELECT plantid, count_time, num_stops, date_payment_made FROM daily_industrial_water_usage WHERE plantid > 184\"\n", "labels": {"reads": [{"table": "daily_industrial_water_usage", "columns": ["plantid", "count_time", "num_stops", "date_payment_made"]}], "writes": [{"table": "artifacts", "columns": ["plantid", "count_time", "num_stops", "date_payment_made"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fault_log_parts SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model exhibition_record depends on artsheritage\ndbt run --select exhibition_record --vars '{\"src\":\"artsheritage\"}'\n", "labels": {"reads": [{"table": "artsheritage", "columns": null}], "writes": [{"table": "exhibition_record", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM eia_schedule\"\n", "labels": {"reads": [{"table": "eia_schedule", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT production_value, main_industry FROM has_amenity LIMIT 136;\nINSERT INTO issues SELECT class_code, medicine_id FROM vaccinations WHERE class_code > 109;\nSQL\n", "labels": {"reads": [{"table": "has_amenity", "columns": ["production_value", "main_industry"]}, {"table": "vaccinations", "columns": ["class_code", "medicine_id"]}], "writes": [{"table": "issues", "columns": ["class_code", "medicine_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO bi.products_daily SELECT a.attack_count, b.inclusive FROM broadband_subscribers a JOIN ods.ods_payments_full b ON a.capacity = b.capacity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "broadband_subscribers", "columns": null}, {"table": "ods.ods_payments_full", "columns": null}], "writes": [{"table": "bi.products_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO exhibition_visits SELECT cargo_weight, publication_date FROM stg.campaigns_df WHERE cargo_weight > 480\"], check=True)\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": ["cargo_weight", "publication_date"]}], "writes": [{"table": "exhibition_visits", "columns": ["cargo_weight", "publication_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sex, union_name FROM rating LIMIT 98\")\nimport logging\nspark.sql(\"INSERT INTO artistsdemographics SELECT policy_description, lanes, post_date FROM customers_cards WHERE policy_description > 173\")\n", "labels": {"reads": [{"table": "rating", "columns": ["sex", "union_name"]}, {"table": "customers_cards", "columns": ["policy_description", "lanes", "post_date"]}], "writes": [{"table": "artistsdemographics", "columns": ["policy_description", "lanes", "post_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.emp_id > 360).all()\n# src table: renewable_energy_projects\nengine.execute(\"INSERT INTO department_stores SELECT * FROM renewable_energy_projects\")\n", "labels": {"reads": [{"table": "renewable_energy_projects", "columns": null}], "writes": [{"table": "department_stores", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"fossil_fuel_vehicles_japan\")\nupsert_to_output(df, \"public.crime_types\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles_japan", "columns": null}], "writes": [{"table": "public.crime_types", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO busmaintenance (vendorname, coalquantity) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "busmaintenance", "columns": ["vendorname", "coalquantity"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicles\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"rural_infrastructure\")\n", "labels": {"reads": [{"table": "vehicles", "columns": null}], "writes": [{"table": "rural_infrastructure", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT build_date, chemical FROM station LIMIT 344\")\nrows = cur.fetchall()\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "station", "columns": ["build_date", "chemical"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM race_ethnicity\"\n", "labels": {"reads": [{"table": "race_ethnicity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.inventory_hourly (shipped_to, ship_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.inventory_hourly", "columns": ["shipped_to", "ship_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table patient_satisfaction --columns author_or_editor,call_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "patient_satisfaction", "columns": ["author_or_editor", "call_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM researchpapers\", conn)\ndf.to_sql(\"volunteer_hours\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "researchpapers", "columns": null}], "writes": [{"table": "volunteer_hours", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO permian_basin SELECT a.strain_name, b.supplier_phone FROM discipline_enrollments a JOIN emergency_responses b ON a.song_release_year = b.song_release_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "discipline_enrollments", "columns": null}, {"table": "emergency_responses", "columns": null}], "writes": [{"table": "permian_basin", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads_payments_di SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO community_programs (warehouseid, refugee_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "community_programs", "columns": ["warehouseid", "refugee_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_materials\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"financialwellbeing\")\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table parts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "parts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO temperaturehistory SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart.mart_users_di SELECT chair_name, exit_type, property_price, communityid FROM communityengagementmetrics WHERE chair_name > 190\"\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": ["chair_name", "exit_type", "property_price", "communityid"]}], "writes": [{"table": "mart.mart_users_di", "columns": ["chair_name", "exit_type", "property_price", "communityid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT caseid, tonnage FROM permian_basin\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"ads.ads_vendors_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "permian_basin", "columns": ["caseid", "tonnage"]}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"restaurant\")\ndump_to_store(df, \"match_result\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": [{"table": "match_result", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table dwd.dwd_payments_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.dwd_payments_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.degrees > 101).all()\n# src table: all_documents\nengine.execute(\"INSERT INTO tourist_destinations SELECT * FROM all_documents\")\n", "labels": {"reads": [{"table": "all_documents", "columns": null}], "writes": [{"table": "tourist_destinations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO safety_testing (destination_id, hotel_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "safety_testing", "columns": ["destination_id", "hotel_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO regulatory_frameworks SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM musicsales\"\n", "labels": {"reads": [{"table": "musicsales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO green_building_materials SELECT pd_id, contractor, donor_country FROM support_groups WHERE pd_id > 157\"\n", "labels": {"reads": [{"table": "support_groups", "columns": ["pd_id", "contractor", "donor_country"]}], "writes": [{"table": "green_building_materials", "columns": ["pd_id", "contractor", "donor_country"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table projectemployees --target-dir /tmp/land\n", "labels": {"reads": [{"table": "projectemployees", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO talent_acquisition SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_budget_percent_budgeted > 417).all()\n# src table: food_justice\nengine.execute(\"INSERT INTO militaryequipmentsales SELECT * FROM food_justice\")\n", "labels": {"reads": [{"table": "food_justice", "columns": null}], "writes": [{"table": "militaryequipmentsales", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table permit --target-dir /tmp/land\n", "labels": {"reads": [{"table": "permit", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"documents_to_be_destroyed\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"campuses\")\n", "labels": {"reads": [{"table": "documents_to_be_destroyed", "columns": null}], "writes": [{"table": "campuses", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO user_likes SELECT location_text, job, trip_id FROM country_renewable_energy WHERE location_text > 298;\nEOF\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": ["location_text", "job", "trip_id"]}], "writes": [{"table": "user_likes", "columns": ["location_text", "job", "trip_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO market SELECT a.supplierid, b.lawyer_name FROM soccer_teams a JOIN humanitarian_assistance b ON a.cruelty_free = b.cruelty_free\"\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}, {"table": "humanitarian_assistance", "columns": null}], "writes": [{"table": "market", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO shop SELECT 1\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.clublocation > 439).all()\n# src table: intelligence_agents\nengine.execute(\"INSERT INTO caseattorneys SELECT * FROM intelligence_agents\")\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}], "writes": [{"table": "caseattorneys", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT nickname, port_name FROM dwd.dwd_exposure_df LIMIT 111\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": ["nickname", "port_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO address SELECT is_unionized, customer_number FROM police_officers_tx WHERE is_unionized > 201;\nEOF\n", "labels": {"reads": [{"table": "police_officers_tx", "columns": ["is_unionized", "customer_number"]}], "writes": [{"table": "address", "columns": ["is_unionized", "customer_number"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM city_waste_generation\", conn)\ndf.to_sql(\"manufacturers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "city_waste_generation", "columns": null}], "writes": [{"table": "manufacturers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nsql = \"INSERT INTO claims_processing_stages SELECT a.base_id, b.sculpture_name FROM trips a JOIN uniteddefense.equipmentsales b ON a.handling_id = b.handling_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "trips", "columns": null}, {"table": "uniteddefense.equipmentsales", "columns": null}], "writes": [{"table": "claims_processing_stages", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO attorney_billing SELECT sustainability_initiative_id, matchdate, galleryid, citation_time FROM atlantic_ocean_fish WHERE sustainability_initiative_id > 117\"], check=True)\n", "labels": {"reads": [{"table": "atlantic_ocean_fish", "columns": ["sustainability_initiative_id", "matchdate", "galleryid", "citation_time"]}], "writes": [{"table": "attorney_billing", "columns": ["sustainability_initiative_id", "matchdate", "galleryid", "citation_time"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT retailer, faculty_id FROM brands LIMIT 337\")\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ref_locations SELECT country_of_origin, fabricid, aircraft_type, tech_id FROM editor WHERE country_of_origin > 132\")\n", "labels": {"reads": [{"table": "brands", "columns": ["retailer", "faculty_id"]}, {"table": "editor", "columns": ["country_of_origin", "fabricid", "aircraft_type", "tech_id"]}], "writes": [{"table": "ref_locations", "columns": ["country_of_origin", "fabricid", "aircraft_type", "tech_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO languages SELECT * FROM legacy\ncur.execute(\"SELECT role_name, headquarters FROM military_bases LIMIT 31\")\n", "labels": {"reads": [{"table": "military_bases", "columns": ["role_name", "headquarters"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO stg.risk_score_hourly SELECT created_date, sustainabilityrating FROM fabrics WHERE created_date > 470\"\n", "labels": {"reads": [{"table": "fabrics", "columns": ["created_date", "sustainabilityrating"]}], "writes": [{"table": "stg.risk_score_hourly", "columns": ["created_date", "sustainabilityrating"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO e_scooter_trips (retailer, union_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "e_scooter_trips", "columns": ["retailer", "union_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"city_properties\")\nsrc.write.insertInto(\"green_energy_lending_programs\", overwrite=True)\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": [{"table": "green_energy_lending_programs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.image_date > 427).all()\n# src table: dws.dws_orders_full\nengine.execute(\"INSERT INTO weapons SELECT * FROM dws.dws_orders_full\")\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": null}], "writes": [{"table": "weapons", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO emissions SELECT labor_practice, vehicletype FROM workout WHERE labor_practice > 45\"\n", "labels": {"reads": [{"table": "workout", "columns": ["labor_practice", "vehicletype"]}], "writes": [{"table": "emissions", "columns": ["labor_practice", "vehicletype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.stg_users SELECT vehicle_id, destinationid FROM song WHERE vehicle_id > 352;\nEOF\n", "labels": {"reads": [{"table": "song", "columns": ["vehicle_id", "destinationid"]}], "writes": [{"table": "stg.stg_users", "columns": ["vehicle_id", "destinationid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.num_transactions > 134).all()\n# src table: ods_member_point_full\nengine.execute(\"INSERT INTO inmates SELECT * FROM ods_member_point_full\")\n", "labels": {"reads": [{"table": "ods_member_point_full", "columns": null}], "writes": [{"table": "inmates", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO communitydevelopment (capacity_mw, mine_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "communitydevelopment", "columns": ["capacity_mw", "mine_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT race, disaster_id FROM storage_tech LIMIT 257;\nINSERT INTO architect SELECT regulation, claim_date, time_of_day, is_sustainable FROM bike_station_info WHERE regulation > 222;\nSQL\n", "labels": {"reads": [{"table": "storage_tech", "columns": ["race", "disaster_id"]}, {"table": "bike_station_info", "columns": ["regulation", "claim_date", "time_of_day", "is_sustainable"]}], "writes": [{"table": "architect", "columns": ["regulation", "claim_date", "time_of_day", "is_sustainable"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT language_id, line_name FROM satellite_missions_large LIMIT 418\")\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO brand_info SELECT lastname, cargo_weight, forest_id FROM donationsbycause WHERE lastname > 199\")\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": ["language_id", "line_name"]}, {"table": "donationsbycause", "columns": ["lastname", "cargo_weight", "forest_id"]}], "writes": [{"table": "brand_info", "columns": ["lastname", "cargo_weight", "forest_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO algorithmic_fairness SELECT community_id, astronaut_name, trench_name FROM bus_fares WHERE community_id > 203\"\n", "labels": {"reads": [{"table": "bus_fares", "columns": ["community_id", "astronaut_name", "trench_name"]}], "writes": [{"table": "algorithmic_fairness", "columns": ["community_id", "astronaut_name", "trench_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"public.collected_fare\")\nsink_to_warehouse(df, \"party_forms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "public.collected_fare", "columns": null}], "writes": [{"table": "party_forms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cityname > 214).all()\n# src table: employeedata\nengine.execute(\"INSERT INTO conditions SELECT * FROM employeedata\")\n", "labels": {"reads": [{"table": "employeedata", "columns": null}], "writes": [{"table": "conditions", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO platformg SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"media_types\")\nsrc.write.insertInto(\"ods_risk_score_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "media_types", "columns": null}], "writes": [{"table": "ods_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model city depends on mart.shipments_delta\ndbt build -s city --vars '{\"source_table\":\"mart.shipments_delta\"}'\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": null}], "writes": [{"table": "city", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"smart_city_projects\")\nsave_to_target(df, \"green_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": null}], "writes": [{"table": "green_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO worker_scores SELECT fund_name, trip_date, line_1_number_building, num_of_component FROM bi.bi_sessions_daily WHERE fund_name > 10\"], check=True)\n", "labels": {"reads": [{"table": "bi.bi_sessions_daily", "columns": ["fund_name", "trip_date", "line_1_number_building", "num_of_component"]}], "writes": [{"table": "worker_scores", "columns": ["fund_name", "trip_date", "line_1_number_building", "num_of_component"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO topublictransportation SELECT genderid, booking_start_date, annual_carbon_offsets FROM instructor WHERE genderid > 118\"\n", "labels": {"reads": [{"table": "instructor", "columns": ["genderid", "booking_start_date", "annual_carbon_offsets"]}], "writes": [{"table": "topublictransportation", "columns": ["genderid", "booking_start_date", "annual_carbon_offsets"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"atlantic_marine_life\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "atlantic_marine_life", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO plots SELECT 1\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mission SELECT line_name, team_id_br, operating_system, outcome_id FROM company_info WHERE line_name > 76\"], check=True)\n", "labels": {"reads": [{"table": "company_info", "columns": ["line_name", "team_id_br", "operating_system", "outcome_id"]}], "writes": [{"table": "mission", "columns": ["line_name", "team_id_br", "operating_system", "outcome_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table sports --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sports", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO menu_item SELECT emp_fname, last_maintenance FROM manufacturermaterials WHERE emp_fname > 306\"\n", "labels": {"reads": [{"table": "manufacturermaterials", "columns": ["emp_fname", "last_maintenance"]}], "writes": [{"table": "menu_item", "columns": ["emp_fname", "last_maintenance"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO dwd.dwd_campaigns SELECT round_type, building_address, team, productname FROM fish_purchases WHERE round_type > 182\")\n", "labels": {"reads": [{"table": "fish_purchases", "columns": ["round_type", "building_address", "team", "productname"]}], "writes": [{"table": "dwd.dwd_campaigns", "columns": ["round_type", "building_address", "team", "productname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fabricdata SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT movie_id, parent_organization_id FROM ads.ads_exposure_di LIMIT 143\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO stg.stg_users_full SELECT no_of_customers, investor_id, statement_details, organizationid FROM virtual_tourism WHERE no_of_customers > 411\")\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["movie_id", "parent_organization_id"]}, {"table": "virtual_tourism", "columns": ["no_of_customers", "investor_id", "statement_details", "organizationid"]}], "writes": [{"table": "stg.stg_users_full", "columns": ["no_of_customers", "investor_id", "statement_details", "organizationid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table tb_reports --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tb_reports", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO wholesale_orders (research_name, ll_hours) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "wholesale_orders", "columns": ["research_name", "ll_hours"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO carbon_offset_south_america SELECT tripid, max_gust_speed_mph, exhibitioncountry FROM ads.ads_exposure_daily WHERE tripid > 257\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": ["tripid", "max_gust_speed_mph", "exhibitioncountry"]}], "writes": [{"table": "carbon_offset_south_america", "columns": ["tripid", "max_gust_speed_mph", "exhibitioncountry"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO transportation SELECT gender_diversity, clean_jerk, staff_details FROM organisations WHERE gender_diversity > 484\"\n", "labels": {"reads": [{"table": "organisations", "columns": ["gender_diversity", "clean_jerk", "staff_details"]}], "writes": [{"table": "transportation", "columns": ["gender_diversity", "clean_jerk", "staff_details"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.alid > 468).all()\n# src table: maintenancerequests\nengine.execute(\"INSERT INTO whale_sharks SELECT * FROM maintenancerequests\")\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": null}], "writes": [{"table": "whale_sharks", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT issue_id, serviceid FROM sustainableprojects\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"deliveryaddresses\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sustainableprojects", "columns": ["issue_id", "serviceid"]}], "writes": [{"table": "deliveryaddresses", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO recovery_program (averagespeed, menucategory) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "recovery_program", "columns": ["averagespeed", "menucategory"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT aircraft, sale_quantity FROM supportservices LIMIT 95\")\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO survey_data SELECT num_of_component, credit_score, fastestlapspeed, socially_responsible FROM landfillcapacitybycountry WHERE num_of_component > 185\")\n", "labels": {"reads": [{"table": "supportservices", "columns": ["aircraft", "sale_quantity"]}, {"table": "landfillcapacitybycountry", "columns": ["num_of_component", "credit_score", "fastestlapspeed", "socially_responsible"]}], "writes": [{"table": "survey_data", "columns": ["num_of_component", "credit_score", "fastestlapspeed", "socially_responsible"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO beverages SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO canada_tech SELECT founders_lgbtq, carbon_offset_tons FROM fan_purchases WHERE founders_lgbtq > 69;\nEOF\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["founders_lgbtq", "carbon_offset_tons"]}], "writes": [{"table": "canada_tech", "columns": ["founders_lgbtq", "carbon_offset_tons"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"accommodations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "accommodations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"incident_region\")\nexport_to_target(df, \"ticketspending\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "incident_region", "columns": null}], "writes": [{"table": "ticketspending", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stores_2 SELECT minename, sustainabilityrating, forest_id FROM vessels_2 WHERE minename > 19\"\n", "labels": {"reads": [{"table": "vessels_2", "columns": ["minename", "sustainabilityrating", "forest_id"]}], "writes": [{"table": "stores_2", "columns": ["minename", "sustainabilityrating", "forest_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO underwater_trenches SELECT a.water_depth, b.event_type_id FROM urban_transportation a JOIN design_standards b ON a.num_students = b.num_students\"\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}, {"table": "design_standards", "columns": null}], "writes": [{"table": "underwater_trenches", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO traditional_arts (devices, tour_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "traditional_arts", "columns": ["devices", "tour_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT treasurer_vote, donationid FROM exhibition_visitors LIMIT 148\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "exhibition_visitors", "columns": ["treasurer_vote", "donationid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO species_data (hours_served, playdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "species_data", "columns": ["hours_served", "playdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_finance_organizations\"\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT governor, mascot FROM us_cities LIMIT 184\")\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO climate_adaptation_projects SELECT employeeid, contract_value, date_incident_end, tour_type FROM tb_reports WHERE employeeid > 305\")\n", "labels": {"reads": [{"table": "us_cities", "columns": ["governor", "mascot"]}, {"table": "tb_reports", "columns": ["employeeid", "contract_value", "date_incident_end", "tour_type"]}], "writes": [{"table": "climate_adaptation_projects", "columns": ["employeeid", "contract_value", "date_incident_end", "tour_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO genetics.projects SELECT coach_name, hours FROM indigenouscommunities WHERE coach_name > 402\")\n", "labels": {"reads": [{"table": "indigenouscommunities", "columns": ["coach_name", "hours"]}], "writes": [{"table": "genetics.projects", "columns": ["coach_name", "hours"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT community_id, complaint_date FROM vehicle_registrations LIMIT 433\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "vehicle_registrations", "columns": ["community_id", "complaint_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"virtual_visitors\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "virtual_visitors", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT practice_id, disease FROM algorithmic_fairness_incidents_monthly LIMIT 405;\nINSERT INTO sectors SELECT recruitername, developer_id FROM community_engagement WHERE recruitername > 352;\nSQL\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["practice_id", "disease"]}, {"table": "community_engagement", "columns": ["recruitername", "developer_id"]}], "writes": [{"table": "sectors", "columns": ["recruitername", "developer_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"price_data\")\nsrc.write.insertInto(\"animal_budget\", overwrite=True)\n", "labels": {"reads": [{"table": "price_data", "columns": null}], "writes": [{"table": "animal_budget", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT programname, firm FROM public.trips_by_day_train LIMIT 96\")\nimport logging\nspark.sql(\"INSERT INTO airportdata SELECT grant_amount, report_date, received_date FROM match_result WHERE grant_amount > 370\")\n", "labels": {"reads": [{"table": "public.trips_by_day_train", "columns": ["programname", "firm"]}, {"table": "match_result", "columns": ["grant_amount", "report_date", "received_date"]}], "writes": [{"table": "airportdata", "columns": ["grant_amount", "report_date", "received_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mars_rovers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artists_valuation\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "artists_valuation", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO courses SELECT * FROM legacy\ncur.execute(\"SELECT participatedinesports, reo_type FROM catalog_contents LIMIT 280\")\n", "labels": {"reads": [{"table": "catalog_contents", "columns": ["participatedinesports", "reo_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table financial_capability_programs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.compatible_since_year > 475).all()\n# src table: community_policing\nengine.execute(\"INSERT INTO volunteer_events SELECT * FROM community_policing\")\n", "labels": {"reads": [{"table": "community_policing", "columns": null}], "writes": [{"table": "volunteer_events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO hotel_reviews SELECT vehicletype, catalog_publisher FROM drug_sales WHERE vehicletype > 273\"\n", "labels": {"reads": [{"table": "drug_sales", "columns": ["vehicletype", "catalog_publisher"]}], "writes": [{"table": "hotel_reviews", "columns": ["vehicletype", "catalog_publisher"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cargos SELECT complete_date, plan_id FROM africa_projects WHERE complete_date > 494\"\n", "labels": {"reads": [{"table": "africa_projects", "columns": ["complete_date", "plan_id"]}], "writes": [{"table": "cargos", "columns": ["complete_date", "plan_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO storage_projects SELECT vin, post_category FROM onlineengagement WHERE vin > 51\"\n", "labels": {"reads": [{"table": "onlineengagement", "columns": ["vin", "post_category"]}], "writes": [{"table": "storage_projects", "columns": ["vin", "post_category"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fish_purchases SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"gameattendance\")\ndump_to_store(df, \"wastewater_treatment_plants\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gameattendance", "columns": null}], "writes": [{"table": "wastewater_treatment_plants", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"candidates\").toPandas()\ndf[[\"consider_rate\", \"drugname\"]].to_sql(\"cinema\", engine, index=False)\n", "labels": {"reads": [{"table": "candidates", "columns": null}], "writes": [{"table": "cinema", "columns": ["consider_rate", "drugname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT bookings, gender_diversity FROM eu_data_usage LIMIT 294\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["bookings", "gender_diversity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table shariah_compliant_loans --columns year_founded,innovation --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "shariah_compliant_loans", "columns": ["year_founded", "innovation"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"field5\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "field5", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"higher_ed.publications\").toPandas()\ndf[[\"target_name\", \"consultations\"]].to_sql(\"store\", engine, index=False)\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": null}], "writes": [{"table": "store", "columns": ["target_name", "consultations"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "investments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"web_client_accelerator\")\nsrc.write.insertInto(\"mart.campaigns_di\", overwrite=True)\n", "labels": {"reads": [{"table": "web_client_accelerator", "columns": null}], "writes": [{"table": "mart.campaigns_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.method_id > 381).all()\n# src table: technology_access\nengine.execute(\"INSERT INTO stock SELECT * FROM technology_access\")\n", "labels": {"reads": [{"table": "technology_access", "columns": null}], "writes": [{"table": "stock", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT project_id, storeid FROM landfill_capacity\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"incarcerated\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": ["project_id", "storeid"]}], "writes": [{"table": "incarcerated", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO culturalpractices SELECT carrierid, permits_issued FROM public_transportation_routes WHERE carrierid > 320\"\n", "labels": {"reads": [{"table": "public_transportation_routes", "columns": ["carrierid", "permits_issued"]}], "writes": [{"table": "culturalpractices", "columns": ["carrierid", "permits_issued"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO government.region SELECT 1\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO supportprograms SELECT document_description, emissions, ironid FROM stg.risk_score_di WHERE document_description > 315\"\n", "labels": {"reads": [{"table": "stg.risk_score_di", "columns": ["document_description", "emissions", "ironid"]}], "writes": [{"table": "supportprograms", "columns": ["document_description", "emissions", "ironid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO artistsales SELECT to_address, start, end_time, salesperson FROM soilmoisturedata WHERE to_address > 290;\nEOF\n", "labels": {"reads": [{"table": "soilmoisturedata", "columns": ["to_address", "start", "end_time", "salesperson"]}], "writes": [{"table": "artistsales", "columns": ["to_address", "start", "end_time", "salesperson"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO spacemissions SELECT * FROM legacy\ncur.execute(\"SELECT hotel_name, contractid FROM cuisine LIMIT 306\")\n", "labels": {"reads": [{"table": "cuisine", "columns": ["hotel_name", "contractid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fabric SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.payments_delta SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO port_office SELECT a.item_sold, b.date_of_notes FROM aus_wellbeing a JOIN visits b ON a.participation_id = b.participation_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}, {"table": "visits", "columns": null}], "writes": [{"table": "port_office", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO browser SELECT wind_speed_mph, driller FROM model_fairness WHERE wind_speed_mph > 334;\nEOF\n", "labels": {"reads": [{"table": "model_fairness", "columns": ["wind_speed_mph", "driller"]}], "writes": [{"table": "browser", "columns": ["wind_speed_mph", "driller"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO volunteer_hours SELECT brand, fda_approved FROM prepaid_mobile WHERE brand > 469\"\n", "labels": {"reads": [{"table": "prepaid_mobile", "columns": ["brand", "fda_approved"]}], "writes": [{"table": "volunteer_hours", "columns": ["brand", "fda_approved"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.src_apid > 11).all()\n# src table: bi.bi_events_daily\nengine.execute(\"INSERT INTO products_in_events SELECT * FROM bi.bi_events_daily\")\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": null}], "writes": [{"table": "products_in_events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO investments_esg (stageposition, is_recycled) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "investments_esg", "columns": ["stageposition", "is_recycled"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO branch SELECT a.id, b.film_id FROM artifactanalysis a JOIN tourismproviders b ON a.region = b.region\"\n", "labels": {"reads": [{"table": "artifactanalysis", "columns": null}, {"table": "tourismproviders", "columns": null}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT trip_date, ota_id FROM singer_in_concert LIMIT 447\")\nimport logging\nspark.sql(\"INSERT INTO investor SELECT checkout, governor FROM fault_log_parts WHERE checkout > 335\")\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["trip_date", "ota_id"]}, {"table": "fault_log_parts", "columns": ["checkout", "governor"]}], "writes": [{"table": "investor", "columns": ["checkout", "governor"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM member_data\"\n", "labels": {"reads": [{"table": "member_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM job_change\", conn)\ndf.to_sql(\"circulation_history\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "job_change", "columns": null}], "writes": [{"table": "circulation_history", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO episodes (custid, savings) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "episodes", "columns": ["custid", "savings"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO broadband_customers_global SELECT a.fault_short_name, b.fault_log_entry_datetime FROM exhibition a JOIN stg.stg_clicks_delta b ON a.restaurant = b.restaurant\"\n", "labels": {"reads": [{"table": "exhibition", "columns": null}, {"table": "stg.stg_clicks_delta", "columns": null}], "writes": [{"table": "broadband_customers_global", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM music_festival\"\n", "labels": {"reads": [{"table": "music_festival", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO communityhealthworkerscanada SELECT date_formed, resource, bank_name FROM volunteer_hours WHERE date_formed > 462\"\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["date_formed", "resource", "bank_name"]}], "writes": [{"table": "communityhealthworkerscanada", "columns": ["date_formed", "resource", "bank_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.custid > 6).all()\n# src table: healthcare_centers\nengine.execute(\"INSERT INTO urbanagricrop SELECT * FROM healthcare_centers\")\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": null}], "writes": [{"table": "urbanagricrop", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table customer_month --target-dir /tmp/land\n", "labels": {"reads": [{"table": "customer_month", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dwd_risk_score_hourly SELECT aid, investmentid, productionrate FROM dwd.coupon_use_full WHERE aid > 290;\nEOF\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["aid", "investmentid", "productionrate"]}], "writes": [{"table": "dwd_risk_score_hourly", "columns": ["aid", "investmentid", "productionrate"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO vehicle_sales SELECT use_date, closuredate, incidentdate, farm_id FROM militaryoperations WHERE use_date > 175\")\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["use_date", "closuredate", "incidentdate", "farm_id"]}], "writes": [{"table": "vehicle_sales", "columns": ["use_date", "closuredate", "incidentdate", "farm_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fertilizer_usage SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO rural_clinics (client, energy_production) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "rural_clinics", "columns": ["client", "energy_production"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"producersnewmexico\")\nwrite_to_output(df, \"mart.mart_vendors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "producersnewmexico", "columns": null}], "writes": [{"table": "mart.mart_vendors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.ods_campaigns_delta SELECT a.contract_value, b.prof_num FROM ancient_artifacts a JOIN budgets b ON a.alid = b.alid\"\n", "labels": {"reads": [{"table": "ancient_artifacts", "columns": null}, {"table": "budgets", "columns": null}], "writes": [{"table": "ods.ods_campaigns_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO coal_reserves SELECT stat_type, market FROM plants WHERE stat_type > 188\"\n", "labels": {"reads": [{"table": "plants", "columns": ["stat_type", "market"]}], "writes": [{"table": "coal_reserves", "columns": ["stat_type", "market"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"solana_transactions\")\nsrc.write.insertInto(\"country_labor\", overwrite=True)\n", "labels": {"reads": [{"table": "solana_transactions", "columns": null}], "writes": [{"table": "country_labor", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table defense_personnel --target-dir /tmp/land\n", "labels": {"reads": [{"table": "defense_personnel", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO wholesale_orders SELECT end_date, num_owners, cuisine, serviceid FROM mart.mart_payments_df WHERE end_date > 459\"\n", "labels": {"reads": [{"table": "mart.mart_payments_df", "columns": ["end_date", "num_owners", "cuisine", "serviceid"]}], "writes": [{"table": "wholesale_orders", "columns": ["end_date", "num_owners", "cuisine", "serviceid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vaccine_administered\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vaccine_administered", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM protein\"\n", "labels": {"reads": [{"table": "protein", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO menu SELECT a.postal_code, b.project_education FROM bus_routes a JOIN vrplayers b ON a.energy_star_rating = b.energy_star_rating\"\n", "labels": {"reads": [{"table": "bus_routes", "columns": null}, {"table": "vrplayers", "columns": null}], "writes": [{"table": "menu", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO container_ships SELECT a.work_type, b.item_price FROM participation a JOIN maintenance_engineers b ON a.iata = b.iata\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "participation", "columns": null}, {"table": "maintenance_engineers", "columns": null}], "writes": [{"table": "container_ships", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"ods.ods_users_daily\")\nsave_to_sink(df, \"safety_violations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.ods_users_daily", "columns": null}], "writes": [{"table": "safety_violations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table food_production --target-dir /tmp/land\n", "labels": {"reads": [{"table": "food_production", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ethicalaibudget SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM address\"\n", "labels": {"reads": [{"table": "address", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_coupon_use_di\").toPandas()\ndf[[\"ycard\", \"payment_type_code\"]].to_sql(\"multimodalhubs\", engine, index=False)\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_di", "columns": null}], "writes": [{"table": "multimodalhubs", "columns": ["ycard", "payment_type_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stg.stg_inventory_full\")\nsrc.write.insertInto(\"operations\", overwrite=True)\n", "labels": {"reads": [{"table": "stg.stg_inventory_full", "columns": null}], "writes": [{"table": "operations", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"crops_year\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"military_equipment_maintenance\")\n", "labels": {"reads": [{"table": "crops_year", "columns": null}], "writes": [{"table": "military_equipment_maintenance", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"economic_diversification\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ticket_sales\")\n", "labels": {"reads": [{"table": "economic_diversification", "columns": null}], "writes": [{"table": "ticket_sales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO roller_coaster SELECT home_games, resolutiondate, policy_type_code FROM biosensor.patents WHERE home_games > 249\"\n", "labels": {"reads": [{"table": "biosensor.patents", "columns": ["home_games", "resolutiondate", "policy_type_code"]}], "writes": [{"table": "roller_coaster", "columns": ["home_games", "resolutiondate", "policy_type_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO faculty SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table vesselfuel --columns policy_holder_id,injured --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "vesselfuel", "columns": ["policy_holder_id", "injured"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO storage_projects SELECT * FROM legacy\ncur.execute(\"SELECT characteristic_id, phone FROM participation LIMIT 228\")\n", "labels": {"reads": [{"table": "participation", "columns": ["characteristic_id", "phone"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"artwork\")\nsrc.write.insertInto(\"apac_hotel_views\", overwrite=True)\n", "labels": {"reads": [{"table": "artwork", "columns": null}], "writes": [{"table": "apac_hotel_views", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT name, damage_millions_usd FROM ecohousing LIMIT 237\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ecohousing", "columns": ["name", "damage_millions_usd"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT minister, characteristic_data_type FROM employee LIMIT 78\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "employee", "columns": ["minister", "characteristic_data_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO peakhours SELECT a.testtype, b.max_age FROM mart.mart_refunds_di a JOIN rural_hospitals b ON a.production_qty = b.production_qty\"\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": null}, {"table": "rural_hospitals", "columns": null}], "writes": [{"table": "peakhours", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads.sessions_hourly (port, quality) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads.sessions_hourly", "columns": ["port", "quality"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.inventory_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.inventory_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO eventdates SELECT budget_type_description, contractor FROM marine_species WHERE budget_type_description > 124\"\n", "labels": {"reads": [{"table": "marine_species", "columns": ["budget_type_description", "contractor"]}], "writes": [{"table": "eventdates", "columns": ["budget_type_description", "contractor"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.channel > 92).all()\n# src table: healthbudget\nengine.execute(\"INSERT INTO marine_mammals SELECT * FROM healthbudget\")\n", "labels": {"reads": [{"table": "healthbudget", "columns": null}], "writes": [{"table": "marine_mammals", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"regular_order_products\")\nsrc.write.insertInto(\"reo_production\", overwrite=True)\n", "labels": {"reads": [{"table": "regular_order_products", "columns": null}], "writes": [{"table": "reo_production", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cultural_competency_training\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"freightforwarding\")\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": null}], "writes": [{"table": "freightforwarding", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sustainableproduction\", conn)\ndf.to_sql(\"marinespeciesobservations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sustainableproduction", "columns": null}], "writes": [{"table": "marinespeciesobservations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT drill_count, name_first FROM voting_data LIMIT 175;\nINSERT INTO haircaresales SELECT feature_details, engineer_id FROM has_allergy WHERE feature_details > 406;\nSQL\n", "labels": {"reads": [{"table": "voting_data", "columns": ["drill_count", "name_first"]}, {"table": "has_allergy", "columns": ["feature_details", "engineer_id"]}], "writes": [{"table": "haircaresales", "columns": ["feature_details", "engineer_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT interest_group, project_name FROM passenger_trips LIMIT 55\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "passenger_trips", "columns": ["interest_group", "project_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT judge_id, away_team_score FROM wine LIMIT 155\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO dwd.coupon_use_full SELECT num_attendees, time_stamp, restaurant, oct FROM communication_scores WHERE num_attendees > 343\")\n", "labels": {"reads": [{"table": "wine", "columns": ["judge_id", "away_team_score"]}, {"table": "communication_scores", "columns": ["num_attendees", "time_stamp", "restaurant", "oct"]}], "writes": [{"table": "dwd.coupon_use_full", "columns": ["num_attendees", "time_stamp", "restaurant", "oct"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO publications SELECT email, funding_amount, material_type, emp_lname FROM has_allergy WHERE email > 316\"\n", "labels": {"reads": [{"table": "has_allergy", "columns": ["email", "funding_amount", "material_type", "emp_lname"]}], "writes": [{"table": "publications", "columns": ["email", "funding_amount", "material_type", "emp_lname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM legal_aid_organizations\"\n", "labels": {"reads": [{"table": "legal_aid_organizations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO buildingpermits SELECT discovery_date, rating_id FROM healthcareaccess WHERE discovery_date > 233\"\n", "labels": {"reads": [{"table": "healthcareaccess", "columns": ["discovery_date", "rating_id"]}], "writes": [{"table": "buildingpermits", "columns": ["discovery_date", "rating_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT citation_time, year_built FROM stg.events_hourly LIMIT 281;\nINSERT INTO dwd.inventory_df SELECT container_count, book_title, process_id FROM waterconservation WHERE container_count > 119;\nSQL\n", "labels": {"reads": [{"table": "stg.events_hourly", "columns": ["citation_time", "year_built"]}, {"table": "waterconservation", "columns": ["container_count", "book_title", "process_id"]}], "writes": [{"table": "dwd.inventory_df", "columns": ["container_count", "book_title", "process_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO creative_ai SELECT vrgameid, alert_id, document_type_code FROM country_labor WHERE vrgameid > 307\"\n", "labels": {"reads": [{"table": "country_labor", "columns": ["vrgameid", "alert_id", "document_type_code"]}], "writes": [{"table": "creative_ai", "columns": ["vrgameid", "alert_id", "document_type_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM tourism\", conn)\ndf.to_sql(\"colorado_river_basin\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "tourism", "columns": null}], "writes": [{"table": "colorado_river_basin", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM drivers\", conn)\ndf.to_sql(\"labor_cost\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "drivers", "columns": null}], "writes": [{"table": "labor_cost", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT noise_level, request_id FROM cuisine LIMIT 256\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "cuisine", "columns": ["noise_level", "request_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM property_community\", conn)\ndf.to_sql(\"classroom\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "property_community", "columns": null}], "writes": [{"table": "classroom", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table construction_labor --target-dir /tmp/land\n", "labels": {"reads": [{"table": "construction_labor", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM retailers\", conn)\ndf.to_sql(\"project_timelines\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "retailers", "columns": null}], "writes": [{"table": "project_timelines", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO yearly_production SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sustainability_fact\", conn)\ndf.to_sql(\"clothingsales\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sustainability_fact", "columns": null}], "writes": [{"table": "clothingsales", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hospitals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"traveler\")\n", "labels": {"reads": [{"table": "hospitals", "columns": null}], "writes": [{"table": "traveler", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO tokyo_motor_show SELECT branch_id, itemid, secretary_vote FROM fruitimport WHERE branch_id > 228\"\n", "labels": {"reads": [{"table": "fruitimport", "columns": ["branch_id", "itemid", "secretary_vote"]}], "writes": [{"table": "tokyo_motor_show", "columns": ["branch_id", "itemid", "secretary_vote"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT opening_hours, recycler_id FROM bi.bi_events_daily LIMIT 252\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["opening_hours", "recycler_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd_payments_delta SELECT donationyear, acidity_level, discovered_date FROM solar_plants WHERE donationyear > 47\"], check=True)\n", "labels": {"reads": [{"table": "solar_plants", "columns": ["donationyear", "acidity_level", "discovered_date"]}], "writes": [{"table": "dwd_payments_delta", "columns": ["donationyear", "acidity_level", "discovered_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cerium_production SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fertilizer_usage\", conn)\ndf.to_sql(\"dw_users_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fertilizer_usage", "columns": null}], "writes": [{"table": "dw_users_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"innovation_grants\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_orders\")\n", "labels": {"reads": [{"table": "innovation_grants", "columns": null}], "writes": [{"table": "ads.ads_orders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO exhibition SELECT minename, local_authority, floors, call_time FROM school_districts WHERE minename > 59\"\n", "labels": {"reads": [{"table": "school_districts", "columns": ["minename", "local_authority", "floors", "call_time"]}], "writes": [{"table": "exhibition", "columns": ["minename", "local_authority", "floors", "call_time"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table precipitation_data --columns mappingid,petid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "precipitation_data", "columns": ["mappingid", "petid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT days_held, inclusive FROM studies LIMIT 462\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO product_ingredient SELECT stars, count_date FROM fund_investments WHERE stars > 97\")\n", "labels": {"reads": [{"table": "studies", "columns": ["days_held", "inclusive"]}, {"table": "fund_investments", "columns": ["stars", "count_date"]}], "writes": [{"table": "product_ingredient", "columns": ["stars", "count_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO program_budget (model_id, diplomacy_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "program_budget", "columns": ["model_id", "diplomacy_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"eco_diversification_investment\")\ndump_to_target(df, \"brand_info\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "eco_diversification_investment", "columns": null}], "writes": [{"table": "brand_info", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO menu_engineering SELECT policy_name, register_year, service_type_description FROM africa_schema.african_mines WHERE policy_name > 36\"\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": ["policy_name", "register_year", "service_type_description"]}], "writes": [{"table": "menu_engineering", "columns": ["policy_name", "register_year", "service_type_description"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_events_di\").toPandas()\ndf[[\"event_date\", \"bedtype\"]].to_sql(\"defense_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "dw.dw_events_di", "columns": null}], "writes": [{"table": "defense_projects", "columns": ["event_date", "bedtype"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT trainingid, max_speed FROM museums LIMIT 224\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "museums", "columns": ["trainingid", "max_speed"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.ads_member_point_daily\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"eventparticipation\")\n", "labels": {"reads": [{"table": "ads.ads_member_point_daily", "columns": null}], "writes": [{"table": "eventparticipation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model regional_archaeologists depends on stg.device_log_df\ndbt run --models regional_archaeologists --vars 'source: stg.device_log_df'\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": null}], "writes": [{"table": "regional_archaeologists", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads.risk_score SELECT zone_id, amount_paid, sex FROM mining_companies WHERE zone_id > 185;\nEOF\n", "labels": {"reads": [{"table": "mining_companies", "columns": ["zone_id", "amount_paid", "sex"]}], "writes": [{"table": "ads.risk_score", "columns": ["zone_id", "amount_paid", "sex"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"game_sessions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT temporary_acting, org FROM ads LIMIT 193\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ads", "columns": ["temporary_acting", "org"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ingredients\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sponsorship_donations\")\n", "labels": {"reads": [{"table": "ingredients", "columns": null}], "writes": [{"table": "sponsorship_donations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO biomes SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO highways (order_item_id, attribute_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "highways", "columns": ["order_item_id", "attribute_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO african_union_countries (actor_name, sent_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "african_union_countries", "columns": ["actor_name", "sent_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.lastname > 359).all()\n# src table: daily_industrial_water_usage\nengine.execute(\"INSERT INTO mental_health_center SELECT * FROM daily_industrial_water_usage\")\n", "labels": {"reads": [{"table": "daily_industrial_water_usage", "columns": null}], "writes": [{"table": "mental_health_center", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fault_log_parts SELECT a.shipment_year, b.transaction_product FROM show a JOIN miningwaterusage b ON a.line_1 = b.line_1\"\n", "labels": {"reads": [{"table": "show", "columns": null}, {"table": "miningwaterusage", "columns": null}], "writes": [{"table": "fault_log_parts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table machinery --target-dir /tmp/land\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT major, component_name FROM supplychainemployees LIMIT 454;\nINSERT INTO storage_projects SELECT contract_count, emergency_type, track_id FROM production WHERE contract_count > 59;\nSQL\n", "labels": {"reads": [{"table": "supplychainemployees", "columns": ["major", "component_name"]}, {"table": "production", "columns": ["contract_count", "emergency_type", "track_id"]}], "writes": [{"table": "storage_projects", "columns": ["contract_count", "emergency_type", "track_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO phishing_targets SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_member_point_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO indigenous_food_systems SELECT soil_moisture, vehicle_name, patient FROM safetyorgs WHERE soil_moisture > 242\"\n", "labels": {"reads": [{"table": "safetyorgs", "columns": ["soil_moisture", "vehicle_name", "patient"]}], "writes": [{"table": "indigenous_food_systems", "columns": ["soil_moisture", "vehicle_name", "patient"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO family_cases SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.region_id > 104).all()\n# src table: platformg\nengine.execute(\"INSERT INTO show SELECT * FROM platformg\")\n", "labels": {"reads": [{"table": "platformg", "columns": null}], "writes": [{"table": "show", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO communityhealthworkers SELECT indigenous, vin, contractor_id FROM art_exhibit_attendance WHERE indigenous > 60\"\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["indigenous", "vin", "contractor_id"]}], "writes": [{"table": "communityhealthworkers", "columns": ["indigenous", "vin", "contractor_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO concert_events SELECT engagement_date, mission_name, made_in_usa, coach_id FROM diversion_programs WHERE engagement_date > 139;\nEOF\n", "labels": {"reads": [{"table": "diversion_programs", "columns": ["engagement_date", "mission_name", "made_in_usa", "coach_id"]}], "writes": [{"table": "concert_events", "columns": ["engagement_date", "mission_name", "made_in_usa", "coach_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model products_di depends on shops\ndbt run -s products_di --vars '{\"source_table\":\"shops\"}'\n", "labels": {"reads": [{"table": "shops", "columns": null}], "writes": [{"table": "products_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"caribbeansea\").toPandas()\ndf[[\"shipping_mode\", \"fundingid\"]].to_sql(\"ports\", engine, index=False)\n", "labels": {"reads": [{"table": "caribbeansea", "columns": null}], "writes": [{"table": "ports", "columns": ["shipping_mode", "fundingid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"funding_records\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"gamestats\")\n", "labels": {"reads": [{"table": "funding_records", "columns": null}], "writes": [{"table": "gamestats", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT propertyid, artifactid FROM video_content LIMIT 425\")\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO field_production SELECT cargo_weight, laborproductivity, transaction_amount FROM vr_tech WHERE cargo_weight > 399\")\n", "labels": {"reads": [{"table": "video_content", "columns": ["propertyid", "artifactid"]}, {"table": "vr_tech", "columns": ["cargo_weight", "laborproductivity", "transaction_amount"]}], "writes": [{"table": "field_production", "columns": ["cargo_weight", "laborproductivity", "transaction_amount"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"haircaresales\")\npush_to_target(df, \"travel_advisory\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "haircaresales", "columns": null}], "writes": [{"table": "travel_advisory", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO investor SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO waste SELECT * FROM legacy\ncur.execute(\"SELECT campaign_id, product_category_description FROM materials_usage LIMIT 379\")\n", "labels": {"reads": [{"table": "materials_usage", "columns": ["campaign_id", "product_category_description"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model efforts depends on staff_department_assignments\ndbt run -s efforts --vars '{\"source_table\":\"staff_department_assignments\"}'\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": null}], "writes": [{"table": "efforts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nsql = \"INSERT INTO product_details SELECT a.detection_date, b.issued_date FROM carbon_sequestration a JOIN engineer_skills b ON a.permit_id = b.permit_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "carbon_sequestration", "columns": null}, {"table": "engineer_skills", "columns": null}], "writes": [{"table": "product_details", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO erc20_transactions (advocate_id, rural) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "erc20_transactions", "columns": ["advocate_id", "rural"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"classicgame\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO inclusivehousing.affordablehousing SELECT shipment_id, machine_series FROM companies WHERE shipment_id > 368\")\n", "labels": {"reads": [{"table": "companies", "columns": ["shipment_id", "machine_series"]}], "writes": [{"table": "inclusivehousing.affordablehousing", "columns": ["shipment_id", "machine_series"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO healthcare SELECT fiscal_year, document_type_name FROM catalog_structure WHERE fiscal_year > 339\"\n", "labels": {"reads": [{"table": "catalog_structure", "columns": ["fiscal_year", "document_type_name"]}], "writes": [{"table": "healthcare", "columns": ["fiscal_year", "document_type_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT billingcountry, crs_code FROM news_stories\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"dws.device_log_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "news_stories", "columns": ["billingcountry", "crs_code"]}], "writes": [{"table": "dws.device_log_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table culturalcompetency --target-dir /tmp/land\n", "labels": {"reads": [{"table": "culturalcompetency", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO crypto_transactions SELECT entryid, cases_handled, manager_name, item_sold FROM regulatory_frameworks WHERE entryid > 221\"\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": ["entryid", "cases_handled", "manager_name", "item_sold"]}], "writes": [{"table": "crypto_transactions", "columns": ["entryid", "cases_handled", "manager_name", "item_sold"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"co2price\")\nsrc.write.insertInto(\"nutritionfacts\", overwrite=True)\n", "labels": {"reads": [{"table": "co2price", "columns": null}], "writes": [{"table": "nutritionfacts", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO researcher SELECT events, login_name, sales_details FROM facility WHERE events > 108;\nEOF\n", "labels": {"reads": [{"table": "facility", "columns": ["events", "login_name", "sales_details"]}], "writes": [{"table": "researcher", "columns": ["events", "login_name", "sales_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bridges SELECT volunteer_name, thefttype, healthcareid FROM transportation WHERE volunteer_name > 225\"\n", "labels": {"reads": [{"table": "transportation", "columns": ["volunteer_name", "thefttype", "healthcareid"]}], "writes": [{"table": "bridges", "columns": ["volunteer_name", "thefttype", "healthcareid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM consumer_preference\"\n", "labels": {"reads": [{"table": "consumer_preference", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO research_grants SELECT menu_name, totaldonation, trip_distance FROM product_catalog WHERE menu_name > 147;\nEOF\n", "labels": {"reads": [{"table": "product_catalog", "columns": ["menu_name", "totaldonation", "trip_distance"]}], "writes": [{"table": "research_grants", "columns": ["menu_name", "totaldonation", "trip_distance"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO grapes SELECT advocate_id, stationid FROM dwd.dwd_coupon_use_df WHERE advocate_id > 252;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_coupon_use_df", "columns": ["advocate_id", "stationid"]}], "writes": [{"table": "grapes", "columns": ["advocate_id", "stationid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT year, class_president_vote FROM textile_sourcing LIMIT 132;\nINSERT INTO stg.risk_score_hourly SELECT company_type_code, purchases, size_ha FROM autonomousvehicles WHERE company_type_code > 467;\nSQL\n", "labels": {"reads": [{"table": "textile_sourcing", "columns": ["year", "class_president_vote"]}, {"table": "autonomousvehicles", "columns": ["company_type_code", "purchases", "size_ha"]}], "writes": [{"table": "stg.risk_score_hourly", "columns": ["company_type_code", "purchases", "size_ha"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table constructorstandings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "constructorstandings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_refunds_daily SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"exhibition_record\")\nexport_to_store(df, \"ocean_depths\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "exhibition_record", "columns": null}], "writes": [{"table": "ocean_depths", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT releasedate, booking_start_date FROM exit\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"mart.mart_member_point_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "exit", "columns": ["releasedate", "booking_start_date"]}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO trucks SELECT led_by, call_id, review_id FROM sustainable_urban_properties_2 WHERE led_by > 307\")\n", "labels": {"reads": [{"table": "sustainable_urban_properties_2", "columns": ["led_by", "call_id", "review_id"]}], "writes": [{"table": "trucks", "columns": ["led_by", "call_id", "review_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"employeedata\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw.users_hourly\")\n", "labels": {"reads": [{"table": "employeedata", "columns": null}], "writes": [{"table": "dw.users_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table uniteddefense.equipmentsales --columns to_address,mean_visibility_miles --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "uniteddefense.equipmentsales", "columns": ["to_address", "mean_visibility_miles"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO captain SELECT trench_name, established_date, budget, loan_amount FROM nasa_mars_program WHERE trench_name > 424\"\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": ["trench_name", "established_date", "budget", "loan_amount"]}], "writes": [{"table": "captain", "columns": ["trench_name", "established_date", "budget", "loan_amount"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM safety_incident\", conn)\ndf.to_sql(\"communitydevelopment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}], "writes": [{"table": "communitydevelopment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM art_workshops\"\n", "labels": {"reads": [{"table": "art_workshops", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO unesco_intangible_heritage SELECT screening_id, culturalcompetency, facid, community_center_id FROM street_markets WHERE screening_id > 292;\nEOF\n", "labels": {"reads": [{"table": "street_markets", "columns": ["screening_id", "culturalcompetency", "facid", "community_center_id"]}], "writes": [{"table": "unesco_intangible_heritage", "columns": ["screening_id", "culturalcompetency", "facid", "community_center_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM music_events\"\n", "labels": {"reads": [{"table": "music_events", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO erc20_transactions SELECT a.mission_id, b.sd_id FROM electric_taxis a JOIN recreation_centers b ON a.mental_health_resource_access = b.mental_health_resource_access\"\n", "labels": {"reads": [{"table": "electric_taxis", "columns": null}, {"table": "recreation_centers", "columns": null}], "writes": [{"table": "erc20_transactions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO routes SELECT * FROM legacy\ncur.execute(\"SELECT document_date, mining_operation_id FROM org_donation LIMIT 234\")\n", "labels": {"reads": [{"table": "org_donation", "columns": ["document_date", "mining_operation_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO department_stores SELECT opening_hours, birthday FROM teams WHERE opening_hours > 124\")\n", "labels": {"reads": [{"table": "teams", "columns": ["opening_hours", "birthday"]}], "writes": [{"table": "department_stores", "columns": ["opening_hours", "birthday"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.gdp > 105).all()\n# src table: students_lifelong_learning\nengine.execute(\"INSERT INTO bikerental SELECT * FROM students_lifelong_learning\")\n", "labels": {"reads": [{"table": "students_lifelong_learning", "columns": null}], "writes": [{"table": "bikerental", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO states SELECT individual_id, horizontal_bar_points, healthequitymetricscore FROM solar_plants WHERE individual_id > 130;\nEOF\n", "labels": {"reads": [{"table": "solar_plants", "columns": ["individual_id", "horizontal_bar_points", "healthequitymetricscore"]}], "writes": [{"table": "states", "columns": ["individual_id", "horizontal_bar_points", "healthequitymetricscore"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM contract_states\", conn)\ndf.to_sql(\"maintenance_engineers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "contract_states", "columns": null}], "writes": [{"table": "maintenance_engineers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO round SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT contributions, rehab_date FROM wellbeing_programs LIMIT 169;\nINSERT INTO cargo_tracking SELECT ai_id, framework FROM atlantic_plate WHERE ai_id > 419;\nSQL\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": ["contributions", "rehab_date"]}, {"table": "atlantic_plate", "columns": ["ai_id", "framework"]}], "writes": [{"table": "cargo_tracking", "columns": ["ai_id", "framework"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO record SELECT event_type_id, technique_id FROM stations WHERE event_type_id > 114;\nEOF\n", "labels": {"reads": [{"table": "stations", "columns": ["event_type_id", "technique_id"]}], "writes": [{"table": "record", "columns": ["event_type_id", "technique_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO nomination SELECT enrollment, daily_sales FROM arcticwildlifereserve WHERE enrollment > 450\")\n", "labels": {"reads": [{"table": "arcticwildlifereserve", "columns": ["enrollment", "daily_sales"]}], "writes": [{"table": "nomination", "columns": ["enrollment", "daily_sales"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table rating --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rating", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model characteristics depends on causes\ndbt run -s characteristics --vars 'source: causes'\n", "labels": {"reads": [{"table": "causes", "columns": null}], "writes": [{"table": "characteristics", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO total_consumption SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model zipcodes depends on ref_hotel_star_ratings\ndbt run --models zipcodes --vars '{\"src\":\"ref_hotel_star_ratings\"}'\n", "labels": {"reads": [{"table": "ref_hotel_star_ratings", "columns": null}], "writes": [{"table": "zipcodes", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"ods.shipments_df\")\nupsert_to_target(df, \"arcticwildlifereserve\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}], "writes": [{"table": "arcticwildlifereserve", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mailshot_campaigns\").toPandas()\ndf[[\"funding_round_id\", \"debate_id\"]].to_sql(\"rural_economy_2\", engine, index=False)\n", "labels": {"reads": [{"table": "mailshot_campaigns", "columns": null}], "writes": [{"table": "rural_economy_2", "columns": ["funding_round_id", "debate_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bioprocess_engineering\", conn)\ndf.to_sql(\"labor_hours\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bioprocess_engineering", "columns": null}], "writes": [{"table": "labor_hours", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"episodes\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"trucks\")\n", "labels": {"reads": [{"table": "episodes", "columns": null}], "writes": [{"table": "trucks", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.completion_status > 482).all()\n# src table: dws.dws_orders\nengine.execute(\"INSERT INTO news_stories SELECT * FROM dws.dws_orders\")\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": null}], "writes": [{"table": "news_stories", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"disabilityadvocacy\")\nsrc.write.insertInto(\"mart.clicks\", overwrite=True)\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": null}], "writes": [{"table": "mart.clicks", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO course_attendance SELECT * FROM legacy\ncur.execute(\"SELECT contractor_name, contributiondate FROM doctors LIMIT 197\")\n", "labels": {"reads": [{"table": "doctors", "columns": ["contractor_name", "contributiondate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart_events_full SELECT org_name, tv_show_id FROM news_reporting WHERE org_name > 136\"], check=True)\n", "labels": {"reads": [{"table": "news_reporting", "columns": ["org_name", "tv_show_id"]}], "writes": [{"table": "mart_events_full", "columns": ["org_name", "tv_show_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO water_usage (cruelty_free, fabric_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "water_usage", "columns": ["cruelty_free", "fabric_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM defense_projects\", conn)\ndf.to_sql(\"dws.dws_events_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "defense_projects", "columns": null}], "writes": [{"table": "dws.dws_events_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT style, virtual_tour_engagement_time FROM emissions\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"safety_incidents_india\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "emissions", "columns": ["style", "virtual_tour_engagement_time"]}], "writes": [{"table": "safety_incidents_india", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM resource_extraction\"\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dwd.dwd_orders_daily SELECT a.artist_id, b.emp_hiredate FROM region a JOIN visualartprograms b ON a.allergytype = b.allergytype\"\n", "labels": {"reads": [{"table": "region", "columns": null}, {"table": "visualartprograms", "columns": null}], "writes": [{"table": "dwd.dwd_orders_daily", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model material depends on bi.bi_orders_delta\ndbt run --models material --vars '{\"source_table\":\"bi.bi_orders_delta\"}'\n", "labels": {"reads": [{"table": "bi.bi_orders_delta", "columns": null}], "writes": [{"table": "material", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"clinic_2022\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"item_prices\")\n", "labels": {"reads": [{"table": "clinic_2022", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"low_value_contracts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"network_infrastructure\")\n", "labels": {"reads": [{"table": "low_value_contracts", "columns": null}], "writes": [{"table": "network_infrastructure", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table shariahfinance --columns starttime,away_team_score --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "shariahfinance", "columns": ["starttime", "away_team_score"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT production_budget, agency_id FROM player_award LIMIT 305\")\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO stg.stg_events_hourly SELECT tot_cred, game_genre, transactions, individual_name FROM park WHERE tot_cred > 454\")\n", "labels": {"reads": [{"table": "player_award", "columns": ["production_budget", "agency_id"]}, {"table": "park", "columns": ["tot_cred", "game_genre", "transactions", "individual_name"]}], "writes": [{"table": "stg.stg_events_hourly", "columns": ["tot_cred", "game_genre", "transactions", "individual_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO market_trends (numhearings, subscribe_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "market_trends", "columns": ["numhearings", "subscribe_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO threatintelligence SELECT stu_lname, flightid, capacity_percentage, longitude FROM broadband_plans WHERE stu_lname > 306\")\n", "labels": {"reads": [{"table": "broadband_plans", "columns": ["stu_lname", "flightid", "capacity_percentage", "longitude"]}], "writes": [{"table": "threatintelligence", "columns": ["stu_lname", "flightid", "capacity_percentage", "longitude"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO city_properties (projectname, operation_count) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "city_properties", "columns": ["projectname", "operation_count"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO clothingsales SELECT facility_name, is_unionized, college_location, ship_agent_id FROM bi.users_full WHERE facility_name > 282;\nEOF\n", "labels": {"reads": [{"table": "bi.users_full", "columns": ["facility_name", "is_unionized", "college_location", "ship_agent_id"]}], "writes": [{"table": "clothingsales", "columns": ["facility_name", "is_unionized", "college_location", "ship_agent_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"drivers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"artworks\")\n", "labels": {"reads": [{"table": "drivers", "columns": null}], "writes": [{"table": "artworks", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO violations (minesite, explainability_score) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "violations", "columns": ["minesite", "explainability_score"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sustainable_menu_items (contract_amount, eventdate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sustainable_menu_items", "columns": ["contract_amount", "eventdate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT label_id, customer_id FROM safety_incident LIMIT 46;\nINSERT INTO landfill_capacity_north_america SELECT product_id, name_last FROM attendance WHERE product_id > 344;\nSQL\n", "labels": {"reads": [{"table": "safety_incident", "columns": ["label_id", "customer_id"]}, {"table": "attendance", "columns": ["product_id", "name_last"]}], "writes": [{"table": "landfill_capacity_north_america", "columns": ["product_id", "name_last"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT platformname, acc_regular_season FROM attorney_billing_rates LIMIT 212\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": ["platformname", "acc_regular_season"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"family_cases\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart_orders_di\")\n", "labels": {"reads": [{"table": "family_cases", "columns": null}], "writes": [{"table": "mart_orders_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.date_of_notes > 71).all()\n# src table: nutritionfacts\nengine.execute(\"INSERT INTO carbon_offset_south_america SELECT * FROM nutritionfacts\")\n", "labels": {"reads": [{"table": "nutritionfacts", "columns": null}], "writes": [{"table": "carbon_offset_south_america", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO marine_life_research SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"systems\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"project_duration\")\n", "labels": {"reads": [{"table": "systems", "columns": null}], "writes": [{"table": "project_duration", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO wastewater_facilities SELECT shipped_to, stat_type FROM excavations WHERE shipped_to > 122\"\n", "labels": {"reads": [{"table": "excavations", "columns": ["shipped_to", "stat_type"]}], "writes": [{"table": "wastewater_facilities", "columns": ["shipped_to", "stat_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"on_call\")\nsrc.write.insertInto(\"oceania_countries\", overwrite=True)\n", "labels": {"reads": [{"table": "on_call", "columns": null}], "writes": [{"table": "oceania_countries", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO developers SELECT candidate_id, dependent_name, treatment_id FROM dws_coupon_use WHERE candidate_id > 467\"\n", "labels": {"reads": [{"table": "dws_coupon_use", "columns": ["candidate_id", "dependent_name", "treatment_id"]}], "writes": [{"table": "developers", "columns": ["candidate_id", "dependent_name", "treatment_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.dept_id > 316).all()\n# src table: online_travel_agency\nengine.execute(\"INSERT INTO incidents_by_month SELECT * FROM online_travel_agency\")\n", "labels": {"reads": [{"table": "online_travel_agency", "columns": null}], "writes": [{"table": "incidents_by_month", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"song\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_health_monitor\")\n", "labels": {"reads": [{"table": "song", "columns": null}], "writes": [{"table": "ocean_health_monitor", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO disaster_response_donations SELECT a.environmental_impact, b.operationdate FROM noise_pollution a JOIN clinical_trials b ON a.date_id = b.date_id\"\n", "labels": {"reads": [{"table": "noise_pollution", "columns": null}, {"table": "clinical_trials", "columns": null}], "writes": [{"table": "disaster_response_donations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO solar_farms SELECT 1\"\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO underwater_cables SELECT a.other_details, b.plant_id FROM european_healthcare a JOIN salary b ON a.artifactid = b.artifactid\"\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}, {"table": "salary", "columns": null}], "writes": [{"table": "underwater_cables", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT maintenance_date, workeridentity FROM mart.mart_device_log LIMIT 271\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO member_of SELECT mission, handling_id, loadingstart FROM shariah_compliant_loans WHERE mission > 415\")\n", "labels": {"reads": [{"table": "mart.mart_device_log", "columns": ["maintenance_date", "workeridentity"]}, {"table": "shariah_compliant_loans", "columns": ["mission", "handling_id", "loadingstart"]}], "writes": [{"table": "member_of", "columns": ["mission", "handling_id", "loadingstart"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM rental\"\n", "labels": {"reads": [{"table": "rental", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO insurancetype SELECT fleet_id, awayteamid FROM music_streaming WHERE fleet_id > 429\"\n", "labels": {"reads": [{"table": "music_streaming", "columns": ["fleet_id", "awayteamid"]}], "writes": [{"table": "insurancetype", "columns": ["fleet_id", "awayteamid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bi.bi_inventory_di SELECT pages_per_minute_color, capacity_percentage, community_name, attorney FROM field WHERE pages_per_minute_color > 492\")\n", "labels": {"reads": [{"table": "field", "columns": ["pages_per_minute_color", "capacity_percentage", "community_name", "attorney"]}], "writes": [{"table": "bi.bi_inventory_di", "columns": ["pages_per_minute_color", "capacity_percentage", "community_name", "attorney"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"departments\")\nupsert_to_sink(df, \"ai_safety_incidents\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "departments", "columns": null}], "writes": [{"table": "ai_safety_incidents", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.number_thousands > 332).all()\n# src table: stg.stg_inventory_hourly\nengine.execute(\"INSERT INTO smart_contracts_table SELECT * FROM stg.stg_inventory_hourly\")\n", "labels": {"reads": [{"table": "stg.stg_inventory_hourly", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"debris\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dapps\")\n", "labels": {"reads": [{"table": "debris", "columns": null}], "writes": [{"table": "dapps", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO news_stories SELECT a.prof_num, b.dob FROM vessel_types a JOIN strandings b ON a.bus_number = b.bus_number\"\n", "labels": {"reads": [{"table": "vessel_types", "columns": null}, {"table": "strandings", "columns": null}], "writes": [{"table": "news_stories", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO expenditure (ticket_id, subscriber_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "expenditure", "columns": ["ticket_id", "subscriber_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO design_standards SELECT sessionid, inclusivehousing FROM digitalliteracytraining WHERE sessionid > 334\")\n", "labels": {"reads": [{"table": "digitalliteracytraining", "columns": ["sessionid", "inclusivehousing"]}], "writes": [{"table": "design_standards", "columns": ["sessionid", "inclusivehousing"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.users_full\").toPandas()\ndf[[\"healthcareid\", \"reviews\"]].to_sql(\"lots\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.users_full", "columns": null}], "writes": [{"table": "lots", "columns": ["healthcareid", "reviews"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO disinformation_detection SELECT a.last_inspection_date, b.chair_name FROM shariah_compliant_finance a JOIN eventattendance b ON a.institution = b.institution\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "shariah_compliant_finance", "columns": null}, {"table": "eventattendance", "columns": null}], "writes": [{"table": "disinformation_detection", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_products_full SELECT budgeted, sustainabilityid FROM teacher_professional_development WHERE budgeted > 334\"\n", "labels": {"reads": [{"table": "teacher_professional_development", "columns": ["budgeted", "sustainabilityid"]}], "writes": [{"table": "ads.ads_products_full", "columns": ["budgeted", "sustainabilityid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT volume, market FROM donationprograms\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"dwd.exposure_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "donationprograms", "columns": ["volume", "market"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ap_budget SELECT invoice_date, initiative_name, donor_program FROM divisions WHERE invoice_date > 385;\nEOF\n", "labels": {"reads": [{"table": "divisions", "columns": ["invoice_date", "initiative_name", "donor_program"]}], "writes": [{"table": "ap_budget", "columns": ["invoice_date", "initiative_name", "donor_program"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table topublictransportation --columns gender_group,rehab_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "topublictransportation", "columns": ["gender_group", "rehab_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fleet\").toPandas()\ndf[[\"userid\", \"claimamount\"]].to_sql(\"dwd.dwd_inventory_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "fleet", "columns": null}], "writes": [{"table": "dwd.dwd_inventory_hourly", "columns": ["userid", "claimamount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainabilityratings\").toPandas()\ndf[[\"launched_year\", \"num_virtual_tours\"]].to_sql(\"caribbeansea\", engine, index=False)\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": null}], "writes": [{"table": "caribbeansea", "columns": ["launched_year", "num_virtual_tours"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"soccer_teams\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws_shipments_df\")\n", "labels": {"reads": [{"table": "soccer_teams", "columns": null}], "writes": [{"table": "dws_shipments_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"causes\")\nsrc.write.insertInto(\"employees\", overwrite=True)\n", "labels": {"reads": [{"table": "causes", "columns": null}], "writes": [{"table": "employees", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws.cart_item_full\", conn)\ndf.to_sql(\"co2price\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": null}], "writes": [{"table": "co2price", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT mental_health_resource_access, production FROM equipment_sales LIMIT 428;\nINSERT INTO mental_health_professionals_2 SELECT university, fare_amount FROM plays_games WHERE university > 156;\nSQL\n", "labels": {"reads": [{"table": "equipment_sales", "columns": ["mental_health_resource_access", "production"]}, {"table": "plays_games", "columns": ["university", "fare_amount"]}], "writes": [{"table": "mental_health_professionals_2", "columns": ["university", "fare_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"divisions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "divisions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"habitat\").toPandas()\ndf[[\"wins\", \"maintenance_contract_id\"]].to_sql(\"plants\", engine, index=False)\n", "labels": {"reads": [{"table": "habitat", "columns": null}], "writes": [{"table": "plants", "columns": ["wins", "maintenance_contract_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO timber_sales SELECT astronautid, cb_year FROM drug_approvals WHERE astronautid > 227\"\n", "labels": {"reads": [{"table": "drug_approvals", "columns": ["astronautid", "cb_year"]}], "writes": [{"table": "timber_sales", "columns": ["astronautid", "cb_year"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.artist_id > 3).all()\n# src table: hospital\nengine.execute(\"INSERT INTO mineral_extraction_us SELECT * FROM hospital\")\n", "labels": {"reads": [{"table": "hospital", "columns": null}], "writes": [{"table": "mineral_extraction_us", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mexico_regions --columns eco_friendly,sustainable_practice --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mexico_regions", "columns": ["eco_friendly", "sustainable_practice"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model sustainable_building depends on bi.bi_payments\ndbt build -s sustainable_building --vars '{\"src\":\"bi.bi_payments\"}'\n", "labels": {"reads": [{"table": "bi.bi_payments", "columns": null}], "writes": [{"table": "sustainable_building", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tree_species --columns launch_agency,service_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tree_species", "columns": ["launch_agency", "service_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT mineral, cases_handled FROM causes_insert_2 LIMIT 98\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "causes_insert_2", "columns": ["mineral", "cases_handled"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.galleryname > 257).all()\n# src table: defenseprojects\nengine.execute(\"INSERT INTO vehicle SELECT * FROM defenseprojects\")\n", "labels": {"reads": [{"table": "defenseprojects", "columns": null}], "writes": [{"table": "vehicle", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"submission\")\nsrc.write.insertInto(\"criticalincidents\", overwrite=True)\n", "labels": {"reads": [{"table": "submission", "columns": null}], "writes": [{"table": "criticalincidents", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO asset_parts SELECT funding_source_type, store_phone, ingredient_id, street_address FROM dwd.dwd_events_delta WHERE funding_source_type > 88\"\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": ["funding_source_type", "store_phone", "ingredient_id", "street_address"]}], "writes": [{"table": "asset_parts", "columns": ["funding_source_type", "store_phone", "ingredient_id", "street_address"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ocean_shipping.cargo SELECT * FROM legacy\ncur.execute(\"SELECT artworkyear, hashtags FROM waste_generation_metrics LIMIT 199\")\n", "labels": {"reads": [{"table": "waste_generation_metrics", "columns": ["artworkyear", "hashtags"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO landfills SELECT routeid, registration_id FROM subway WHERE routeid > 209\"\n", "labels": {"reads": [{"table": "subway", "columns": ["routeid", "registration_id"]}], "writes": [{"table": "landfills", "columns": ["routeid", "registration_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dispensaries\"\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table tourism_activities --columns product_details,participant_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "tourism_activities", "columns": ["product_details", "participant_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO venture (claim_id, special_features) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "venture", "columns": ["claim_id", "special_features"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO missions SELECT building_manager, total_donation_amount FROM development_hours WHERE building_manager > 275\"], check=True)\n", "labels": {"reads": [{"table": "development_hours", "columns": ["building_manager", "total_donation_amount"]}], "writes": [{"table": "missions", "columns": ["building_manager", "total_donation_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO emerging_markets.digital_assets SELECT a.launch_id, b.attorneyid FROM ods.ods_campaigns_df a JOIN labels b ON a.steps = b.steps\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_df", "columns": null}, {"table": "labels", "columns": null}], "writes": [{"table": "emerging_markets.digital_assets", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table coral_reefs --columns community_center_id,production_volume --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "coral_reefs", "columns": ["community_center_id", "production_volume"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"coffee_prices\").toPandas()\ndf[[\"community_size\", \"subject_id\"]].to_sql(\"electricvehiclestats\", engine, index=False)\n", "labels": {"reads": [{"table": "coffee_prices", "columns": null}], "writes": [{"table": "electricvehiclestats", "columns": ["community_size", "subject_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT order_shipping_charges, report_id FROM sales_region LIMIT 204\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "sales_region", "columns": ["order_shipping_charges", "report_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT room, birth_place FROM regular_order_products LIMIT 30\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO storage_projects SELECT artpiecename, filingdate, star_rating_description, sale_revenue FROM contract_timeline WHERE artpiecename > 81\")\n", "labels": {"reads": [{"table": "regular_order_products", "columns": ["room", "birth_place"]}, {"table": "contract_timeline", "columns": ["artpiecename", "filingdate", "star_rating_description", "sale_revenue"]}], "writes": [{"table": "storage_projects", "columns": ["artpiecename", "filingdate", "star_rating_description", "sale_revenue"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT manufacturer_id, num_pallets FROM gamedesigndata\", engine)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"user_workouts_march\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": ["manufacturer_id", "num_pallets"]}], "writes": [{"table": "user_workouts_march", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ingredientsvegancrueltyfree SELECT water_consumption, bioprocess_name, roomname FROM ads.ads_payments_hourly WHERE water_consumption > 267;\nEOF\n", "labels": {"reads": [{"table": "ads.ads_payments_hourly", "columns": ["water_consumption", "bioprocess_name", "roomname"]}], "writes": [{"table": "ingredientsvegancrueltyfree", "columns": ["water_consumption", "bioprocess_name", "roomname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO college SELECT investor_id, lettergrade FROM uel_top10 WHERE investor_id > 437\"\n", "labels": {"reads": [{"table": "uel_top10", "columns": ["investor_id", "lettergrade"]}], "writes": [{"table": "college", "columns": ["investor_id", "lettergrade"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"wellbeing_program_participants\")\ndump_to_output(df, \"country_renewable_energy\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "wellbeing_program_participants", "columns": null}], "writes": [{"table": "country_renewable_energy", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model daily_articles_by_category depends on wearable_metrics\ndbt run --models daily_articles_by_category --vars '{\"source_table\":\"wearable_metrics\"}'\n", "labels": {"reads": [{"table": "wearable_metrics", "columns": null}], "writes": [{"table": "daily_articles_by_category", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"travel_advisory\")\npersist_to_warehouse(df, \"shark_biomass\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "shark_biomass", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"results\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ads_payments_hourly\")\n", "labels": {"reads": [{"table": "results", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.mart_member_point_df (labor_id, established_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_member_point_df", "columns": ["labor_id", "established_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO light_rail_lines (stars, trip_duration) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "light_rail_lines", "columns": ["stars", "trip_duration"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"furniture\").toPandas()\ndf[[\"donator_name\", \"enzyme_id\"]].to_sql(\"participants_in_events\", engine, index=False)\n", "labels": {"reads": [{"table": "furniture", "columns": null}], "writes": [{"table": "participants_in_events", "columns": ["donator_name", "enzyme_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO food_safety_inspections SELECT spf_level, certification, session_language, total_investment FROM patient_satisfaction WHERE spf_level > 316\"\n", "labels": {"reads": [{"table": "patient_satisfaction", "columns": ["spf_level", "certification", "session_language", "total_investment"]}], "writes": [{"table": "food_safety_inspections", "columns": ["spf_level", "certification", "session_language", "total_investment"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO trainers SELECT assessment_date, education_id FROM org_volunteer WHERE assessment_date > 304;\nEOF\n", "labels": {"reads": [{"table": "org_volunteer", "columns": ["assessment_date", "education_id"]}], "writes": [{"table": "trainers", "columns": ["assessment_date", "education_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hosts > 491).all()\n# src table: feedback\nengine.execute(\"INSERT INTO coral_reefs SELECT * FROM feedback\")\n", "labels": {"reads": [{"table": "feedback", "columns": null}], "writes": [{"table": "coral_reefs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organization\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_orders\")\n", "labels": {"reads": [{"table": "organization", "columns": null}], "writes": [{"table": "ads.ads_orders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table disease_prevalence --target-dir /tmp/land\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"collectivebargaining\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"world_heritage_sites\")\n", "labels": {"reads": [{"table": "collectivebargaining", "columns": null}], "writes": [{"table": "world_heritage_sites", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT donor_country, drought_id FROM ods.ods_device_log_delta\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ndf.to_sql(\"contractorsales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": ["donor_country", "drought_id"]}], "writes": [{"table": "contractorsales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT recipient, treatment FROM climate_finance_organizations LIMIT 250;\nINSERT INTO insurancetype SELECT preference_score, company_name, crs_description FROM aircraftsquadrons WHERE preference_score > 369;\nSQL\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": ["recipient", "treatment"]}, {"table": "aircraftsquadrons", "columns": ["preference_score", "company_name", "crs_description"]}], "writes": [{"table": "insurancetype", "columns": ["preference_score", "company_name", "crs_description"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bridges SELECT * FROM legacy\ncur.execute(\"SELECT report_id, sales_count FROM vessel_positions LIMIT 7\")\n", "labels": {"reads": [{"table": "vessel_positions", "columns": ["report_id", "sales_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO electric_vehicle_stats SELECT capacity_percentage, catalog_id FROM green_certification WHERE capacity_percentage > 499;\nEOF\n", "labels": {"reads": [{"table": "green_certification", "columns": ["capacity_percentage", "catalog_id"]}], "writes": [{"table": "electric_vehicle_stats", "columns": ["capacity_percentage", "catalog_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO autoshow (initiativeid, prod_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "autoshow", "columns": ["initiativeid", "prod_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO students_lifelong_learning SELECT requestid, anomaly FROM energy_efficiency_projects WHERE requestid > 77\")\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": ["requestid", "anomaly"]}], "writes": [{"table": "students_lifelong_learning", "columns": ["requestid", "anomaly"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.primary_conference > 49).all()\n# src table: miningdepartment\nengine.execute(\"INSERT INTO teams_mascots SELECT * FROM miningdepartment\")\n", "labels": {"reads": [{"table": "miningdepartment", "columns": null}], "writes": [{"table": "teams_mascots", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT claim_header_id, ranking FROM therapy_session\", engine)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"chemical_production_5\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "therapy_session", "columns": ["claim_header_id", "ranking"]}], "writes": [{"table": "chemical_production_5", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT shoe_brand, player_api_id FROM dwd_risk_score_hourly LIMIT 440;\nINSERT INTO project_issues SELECT program_type, resource, oppose_rate FROM beverages WHERE program_type > 182;\nSQL\n", "labels": {"reads": [{"table": "dwd_risk_score_hourly", "columns": ["shoe_brand", "player_api_id"]}, {"table": "beverages", "columns": ["program_type", "resource", "oppose_rate"]}], "writes": [{"table": "project_issues", "columns": ["program_type", "resource", "oppose_rate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO communityevents SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.rest_id > 300).all()\n# src table: open_pedagogy\nengine.execute(\"INSERT INTO beauty_products SELECT * FROM open_pedagogy\")\n", "labels": {"reads": [{"table": "open_pedagogy", "columns": null}], "writes": [{"table": "beauty_products", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table conservation --columns running_time,planned_delivery_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "conservation", "columns": ["running_time", "planned_delivery_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT charging_level, allergytype FROM event LIMIT 22\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "event", "columns": ["charging_level", "allergytype"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO league_x SELECT mine_location, company_gender, co_owner_count FROM marketingbudget WHERE mine_location > 255\"\n", "labels": {"reads": [{"table": "marketingbudget", "columns": ["mine_location", "company_gender", "co_owner_count"]}], "writes": [{"table": "league_x", "columns": ["mine_location", "company_gender", "co_owner_count"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT gross_in_dollar, outcome_id FROM demographics\", engine)\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"supportprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "demographics", "columns": ["gross_in_dollar", "outcome_id"]}], "writes": [{"table": "supportprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM volunteer_signups\", conn)\ndf.to_sql(\"article_views\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "volunteer_signups", "columns": null}], "writes": [{"table": "article_views", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model review depends on customer_contact_channels\ndbt build --select review --vars '{\"source_table\":\"customer_contact_channels\"}'\n", "labels": {"reads": [{"table": "customer_contact_channels", "columns": null}], "writes": [{"table": "review", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO orders SELECT a.violation_count, b.clean_date FROM buildings a JOIN trees b ON a.rental_date = b.rental_date\"\n", "labels": {"reads": [{"table": "buildings", "columns": null}, {"table": "trees", "columns": null}], "writes": [{"table": "orders", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO bioprocess_engineering SELECT a.founders_lgbtq, b.artworkyear FROM open_pedagogy_courses a JOIN instructor b ON a.numpieces = b.numpieces\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "open_pedagogy_courses", "columns": null}, {"table": "instructor", "columns": null}], "writes": [{"table": "bioprocess_engineering", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dwd.dwd_exposure_full SELECT * FROM legacy\ncur.execute(\"SELECT cb_year, founder FROM royal_family LIMIT 465\")\n", "labels": {"reads": [{"table": "royal_family", "columns": ["cb_year", "founder"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO demographics SELECT a.don_id, b.researcher FROM infra_diversification a JOIN waste_production b ON a.servicename = b.servicename\"\n", "labels": {"reads": [{"table": "infra_diversification", "columns": null}, {"table": "waste_production", "columns": null}], "writes": [{"table": "demographics", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO procedures SELECT is_cruelty_free, grant_amount, worker_name, shipmenttype FROM lead_mines WHERE is_cruelty_free > 269\"\n", "labels": {"reads": [{"table": "lead_mines", "columns": ["is_cruelty_free", "grant_amount", "worker_name", "shipmenttype"]}], "writes": [{"table": "procedures", "columns": ["is_cruelty_free", "grant_amount", "worker_name", "shipmenttype"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_payments_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"manufacturingplants\")\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": null}], "writes": [{"table": "manufacturingplants", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT customer_first_name, squadron FROM document_locations LIMIT 419\")\nimport logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO tokyo_water_consumption SELECT unit_name, judge_state FROM vessel_incident_count WHERE unit_name > 193\")\n", "labels": {"reads": [{"table": "document_locations", "columns": ["customer_first_name", "squadron"]}, {"table": "vessel_incident_count", "columns": ["unit_name", "judge_state"]}], "writes": [{"table": "tokyo_water_consumption", "columns": ["unit_name", "judge_state"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM productivity\", conn)\ndf.to_sql(\"artists_valuation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "productivity", "columns": null}], "writes": [{"table": "artists_valuation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT marketing_region_descriptrion, building_description FROM criticalincidents\", engine)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"musical\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "criticalincidents", "columns": ["marketing_region_descriptrion", "building_description"]}], "writes": [{"table": "musical", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO assignedto SELECT a.grant_id, b.trend FROM dams a JOIN seamounts b ON a.research_id = b.research_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dams", "columns": null}, {"table": "seamounts", "columns": null}], "writes": [{"table": "assignedto", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM projecttimelinebybudget\"\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO vendorfabrics SELECT addressid, detected_at, date_of_ceremony, incidenttype FROM staff_department_assignments WHERE addressid > 451\"\n", "labels": {"reads": [{"table": "staff_department_assignments", "columns": ["addressid", "detected_at", "date_of_ceremony", "incidenttype"]}], "writes": [{"table": "vendorfabrics", "columns": ["addressid", "detected_at", "date_of_ceremony", "incidenttype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO electric_taxis SELECT origin_city, year_founded, framework_id FROM roads WHERE origin_city > 263\"], check=True)\n", "labels": {"reads": [{"table": "roads", "columns": ["origin_city", "year_founded", "framework_id"]}], "writes": [{"table": "electric_taxis", "columns": ["origin_city", "year_founded", "framework_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO fossil_fuel_vehicles SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM innovation_projects\", conn)\ndf.to_sql(\"document_structures\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "innovation_projects", "columns": null}], "writes": [{"table": "document_structures", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO agri_innov SELECT a.risk_level, b.investment_id FROM staff a JOIN hair_care_sales b ON a.date_incident_start = b.date_incident_start\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "staff", "columns": null}, {"table": "hair_care_sales", "columns": null}], "writes": [{"table": "agri_innov", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO chemical SELECT date_problem_reported, breed, club_id FROM customer WHERE date_problem_reported > 80\")\n", "labels": {"reads": [{"table": "customer", "columns": ["date_problem_reported", "breed", "club_id"]}], "writes": [{"table": "chemical", "columns": ["date_problem_reported", "breed", "club_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT daily_co2_emission, policy_number FROM policyanalysis\", engine)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"design_standards\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "policyanalysis", "columns": ["daily_co2_emission", "policy_number"]}], "writes": [{"table": "design_standards", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO shipment_data SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT donation_date, threat_type FROM stg_payments_hourly LIMIT 321;\nINSERT INTO mart.mart_events_di SELECT stocking_density, ironquantity, order_shipping_charges FROM fashion_trend_data WHERE stocking_density > 123;\nSQL\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": ["donation_date", "threat_type"]}, {"table": "fashion_trend_data", "columns": ["stocking_density", "ironquantity", "order_shipping_charges"]}], "writes": [{"table": "mart.mart_events_di", "columns": ["stocking_density", "ironquantity", "order_shipping_charges"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO carbon_offset_programs SELECT seat_section, co2_emissions, crime_date, oppose_rate FROM rural_infrastructure WHERE seat_section > 75\")\n", "labels": {"reads": [{"table": "rural_infrastructure", "columns": ["seat_section", "co2_emissions", "crime_date", "oppose_rate"]}], "writes": [{"table": "carbon_offset_programs", "columns": ["seat_section", "co2_emissions", "crime_date", "oppose_rate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO smart_contracts_transactions SELECT a.famous_title, b.country_code FROM salesperson a JOIN salinity_readings b ON a.sensor_id = b.sensor_id\"\n", "labels": {"reads": [{"table": "salesperson", "columns": null}, {"table": "salinity_readings", "columns": null}], "writes": [{"table": "smart_contracts_transactions", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO race_ethnicity SELECT * FROM legacy\ncur.execute(\"SELECT saleamount, round_type FROM regions LIMIT 307\")\n", "labels": {"reads": [{"table": "regions", "columns": ["saleamount", "round_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table socialimpactinvestments --columns artist_id,provider_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "socialimpactinvestments", "columns": ["artist_id", "provider_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO product_characteristics SELECT workouttype, round_type, funding_source_type FROM exhibition_visitors WHERE workouttype > 68\"\n", "labels": {"reads": [{"table": "exhibition_visitors", "columns": ["workouttype", "round_type", "funding_source_type"]}], "writes": [{"table": "product_characteristics", "columns": ["workouttype", "round_type", "funding_source_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT museum_name, grantid FROM clinics_sa\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"student_mental_health\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "clinics_sa", "columns": ["museum_name", "grantid"]}], "writes": [{"table": "student_mental_health", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO temperaturerecords SELECT impact, guest_first_name, transaction_id FROM chemicalbatches WHERE impact > 56\"], check=True)\n", "labels": {"reads": [{"table": "chemicalbatches", "columns": ["impact", "guest_first_name", "transaction_id"]}], "writes": [{"table": "temperaturerecords", "columns": ["impact", "guest_first_name", "transaction_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO art_pieces SELECT a.num_fans, b.account_details FROM coffee_prices a JOIN vr_tech b ON a.min_dew_point_f = b.min_dew_point_f\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "coffee_prices", "columns": null}, {"table": "vr_tech", "columns": null}], "writes": [{"table": "art_pieces", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT pettype, allergytype FROM biotech.startups LIMIT 77\")\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO dw.inventory_delta SELECT num_projects, publisher FROM culturalpractices WHERE num_projects > 112\")\n", "labels": {"reads": [{"table": "biotech.startups", "columns": ["pettype", "allergytype"]}, {"table": "culturalpractices", "columns": ["num_projects", "publisher"]}], "writes": [{"table": "dw.inventory_delta", "columns": ["num_projects", "publisher"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sensor_id, clicks FROM production_rare_earth_elements LIMIT 387;\nINSERT INTO genetic.projects SELECT low_temperature, total_points FROM missions WHERE low_temperature > 456;\nSQL\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": ["sensor_id", "clicks"]}, {"table": "missions", "columns": ["low_temperature", "total_points"]}], "writes": [{"table": "genetic.projects", "columns": ["low_temperature", "total_points"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT institution_id, chemical_type FROM instructors\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"az_drought_impact\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "instructors", "columns": ["institution_id", "chemical_type"]}], "writes": [{"table": "az_drought_impact", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table greenbuildings --columns access_count,attendanceid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "greenbuildings", "columns": ["access_count", "attendanceid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO professional_development SELECT tx_id, shop_name, membername, company_id FROM ads_payments_hourly WHERE tx_id > 236\"], check=True)\n", "labels": {"reads": [{"table": "ads_payments_hourly", "columns": ["tx_id", "shop_name", "membername", "company_id"]}], "writes": [{"table": "professional_development", "columns": ["tx_id", "shop_name", "membername", "company_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table state_usage --target-dir /tmp/land\n", "labels": {"reads": [{"table": "state_usage", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO warehouses (art_type, port_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "warehouses", "columns": ["art_type", "port_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reign, end_speed FROM representative LIMIT 207\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "representative", "columns": ["reign", "end_speed"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO hotels SELECT a.support_rep_id, b.extraction_date FROM party a JOIN grants b ON a.show_times_per_day = b.show_times_per_day\"\n", "labels": {"reads": [{"table": "party", "columns": null}, {"table": "grants", "columns": null}], "writes": [{"table": "hotels", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO iron_ore_production SELECT programtype, reported_by_staff_id, ship_id, policy_id FROM digital_divide_initiatives WHERE programtype > 210\"], check=True)\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": ["programtype", "reported_by_staff_id", "ship_id", "policy_id"]}], "writes": [{"table": "iron_ore_production", "columns": ["programtype", "reported_by_staff_id", "ship_id", "policy_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT serviceid, feature_details FROM bi.bi_events_df LIMIT 405\")\nmetrics.append(round(score, 4))\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO medicine_enzyme_interaction SELECT labordate, membership, goldid, visit_month FROM jupiter_missions WHERE labordate > 6\")\n", "labels": {"reads": [{"table": "bi.bi_events_df", "columns": ["serviceid", "feature_details"]}, {"table": "jupiter_missions", "columns": ["labordate", "membership", "goldid", "visit_month"]}], "writes": [{"table": "medicine_enzyme_interaction", "columns": ["labordate", "membership", "goldid", "visit_month"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO threatintelligence SELECT visit_id, num_owners, has_spf, waste_amount FROM spacex_missions WHERE visit_id > 62\"\n", "labels": {"reads": [{"table": "spacex_missions", "columns": ["visit_id", "num_owners", "has_spf", "waste_amount"]}], "writes": [{"table": "threatintelligence", "columns": ["visit_id", "num_owners", "has_spf", "waste_amount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_shipments_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart_exposure_di\")\n", "labels": {"reads": [{"table": "stg.stg_shipments_hourly", "columns": null}], "writes": [{"table": "mart_exposure_di", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO regular_order_products SELECT dlocation, menuid FROM venture WHERE dlocation > 448\"\n", "labels": {"reads": [{"table": "venture", "columns": ["dlocation", "menuid"]}], "writes": [{"table": "regular_order_products", "columns": ["dlocation", "menuid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO tracks SELECT review_id, club_name, grant_start_date, international_passengers FROM military_equipment_maintenance WHERE review_id > 13\")\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": ["review_id", "club_name", "grant_start_date", "international_passengers"]}], "writes": [{"table": "tracks", "columns": ["review_id", "club_name", "grant_start_date", "international_passengers"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT archeologist, incidents FROM market_access\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"degrees\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "market_access", "columns": ["archeologist", "incidents"]}], "writes": [{"table": "degrees", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table wta_serves --target-dir /tmp/land\n", "labels": {"reads": [{"table": "wta_serves", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT document_status_code, number_of_vessels FROM wellbeing_programs LIMIT 361\")\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO professional_development SELECT art_id, cid, time_id, policy FROM inclusivehousing.affordablehousing WHERE art_id > 253\")\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": ["document_status_code", "number_of_vessels"]}, {"table": "inclusivehousing.affordablehousing", "columns": ["art_id", "cid", "time_id", "policy"]}], "writes": [{"table": "professional_development", "columns": ["art_id", "cid", "time_id", "policy"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT funding_source, asset_id FROM exhibitionsartworks\", engine)\nif not rows:\n    logger.warning('empty result')\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"ref_hotel_star_ratings\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": ["funding_source", "asset_id"]}], "writes": [{"table": "ref_hotel_star_ratings", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"freshwater_fish_farms\")\nsrc.write.insertInto(\"acidification_data\", overwrite=True)\n", "labels": {"reads": [{"table": "freshwater_fish_farms", "columns": null}], "writes": [{"table": "acidification_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO causes SELECT date_left_staff, society, investment FROM player_college WHERE date_left_staff > 284\"\n", "labels": {"reads": [{"table": "player_college", "columns": ["date_left_staff", "society", "investment"]}], "writes": [{"table": "causes", "columns": ["date_left_staff", "society", "investment"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO stg.stg_coupon_use_hourly SELECT style, daily_co2_emission FROM atlantic_ocean WHERE style > 106\")\n", "labels": {"reads": [{"table": "atlantic_ocean", "columns": ["style", "daily_co2_emission"]}], "writes": [{"table": "stg.stg_coupon_use_hourly", "columns": ["style", "daily_co2_emission"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO waterconservationbudget SELECT inspectionid, precip FROM visualartprograms WHERE inspectionid > 290\"\n", "labels": {"reads": [{"table": "visualartprograms", "columns": ["inspectionid", "precip"]}], "writes": [{"table": "waterconservationbudget", "columns": ["inspectionid", "precip"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT shipping_agent_name, lat FROM functional_areas\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"record\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["shipping_agent_name", "lat"]}], "writes": [{"table": "record", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.practices > 60).all()\n# src table: culturalcompetencytrainings\nengine.execute(\"INSERT INTO on_call SELECT * FROM culturalcompetencytrainings\")\n", "labels": {"reads": [{"table": "culturalcompetencytrainings", "columns": null}], "writes": [{"table": "on_call", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehiclemodels\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dws.payments_delta\")\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": null}], "writes": [{"table": "dws.payments_delta", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO constructorstandings (discovered_date, sale_price) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "constructorstandings", "columns": ["discovered_date", "sale_price"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO state_usage (vendor_name, avg_depth) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "state_usage", "columns": ["vendor_name", "avg_depth"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT goldquantity, classtype FROM lots\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"animals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "lots", "columns": ["goldquantity", "classtype"]}], "writes": [{"table": "animals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT product_type_code, restaurant_name FROM volunteer_events LIMIT 77\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO satellites_by_country SELECT cinema_id, offset_id FROM healthcare_centers WHERE cinema_id > 67\")\n", "labels": {"reads": [{"table": "volunteer_events", "columns": ["product_type_code", "restaurant_name"]}, {"table": "healthcare_centers", "columns": ["cinema_id", "offset_id"]}], "writes": [{"table": "satellites_by_country", "columns": ["cinema_id", "offset_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT participant_details, center FROM fan_purchases\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"machines\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "fan_purchases", "columns": ["participant_details", "center"]}], "writes": [{"table": "machines", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO electric_taxis SELECT a.ship_id, b.document_date FROM submarine_canyons a JOIN design_standards b ON a.mineid = b.mineid\"\n", "labels": {"reads": [{"table": "submarine_canyons", "columns": null}, {"table": "design_standards", "columns": null}], "writes": [{"table": "electric_taxis", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable_power\").toPandas()\ndf[[\"helpful_votes\", \"ticketprice\"]].to_sql(\"bi.bi_events_df\", engine, index=False)\n", "labels": {"reads": [{"table": "renewable_power", "columns": null}], "writes": [{"table": "bi.bi_events_df", "columns": ["helpful_votes", "ticketprice"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_telescopes\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "space_telescopes", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO defense_spending SELECT mediatorid, astronaut_id, building_manager FROM ref_service_types WHERE mediatorid > 102\"\n", "labels": {"reads": [{"table": "ref_service_types", "columns": ["mediatorid", "astronaut_id", "building_manager"]}], "writes": [{"table": "defense_spending", "columns": ["mediatorid", "astronaut_id", "building_manager"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO stg_users_daily SELECT date_incident_end, period, end_date, speed FROM equipment WHERE date_incident_end > 387\")\n", "labels": {"reads": [{"table": "equipment", "columns": ["date_incident_end", "period", "end_date", "speed"]}], "writes": [{"table": "stg_users_daily", "columns": ["date_incident_end", "period", "end_date", "speed"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO housingaffordability SELECT emp_lname, access_date, characteristic_type_code FROM soilanalysis WHERE emp_lname > 196\")\n", "labels": {"reads": [{"table": "soilanalysis", "columns": ["emp_lname", "access_date", "characteristic_type_code"]}], "writes": [{"table": "housingaffordability", "columns": ["emp_lname", "access_date", "characteristic_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT programarea, ota_name FROM weights LIMIT 341\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dorm_amenity SELECT cinema_id, complaint_id, host_city FROM automation_tech WHERE cinema_id > 4\")\n", "labels": {"reads": [{"table": "weights", "columns": ["programarea", "ota_name"]}, {"table": "automation_tech", "columns": ["cinema_id", "complaint_id", "host_city"]}], "writes": [{"table": "dorm_amenity", "columns": ["cinema_id", "complaint_id", "host_city"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table courtcases --target-dir /tmp/land\n", "labels": {"reads": [{"table": "courtcases", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"landfillcapacitybycountry\")\nsrc.write.insertInto(\"publication\", overwrite=True)\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": null}], "writes": [{"table": "publication", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT engagement_date, species_name FROM ads.ads_exposure_di LIMIT 166;\nINSERT INTO ods.ods_events_daily SELECT album_id, start_date, injured, affiliation FROM ods.member_point_df WHERE album_id > 114;\nSQL\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["engagement_date", "species_name"]}, {"table": "ods.member_point_df", "columns": ["album_id", "start_date", "injured", "affiliation"]}], "writes": [{"table": "ods.ods_events_daily", "columns": ["album_id", "start_date", "injured", "affiliation"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sid, certified FROM dw_vendors_di LIMIT 30\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": ["sid", "certified"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM soccer_goals\"\n", "labels": {"reads": [{"table": "soccer_goals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods_clicks_df\")\nsrc.write.insertInto(\"marine_life_data\", overwrite=True)\n", "labels": {"reads": [{"table": "ods_clicks_df", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model features depends on esports_teams\ndbt run --select features --vars '{\"src\":\"esports_teams\"}'\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}], "writes": [{"table": "features", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.clicks\", conn)\ndf.to_sql(\"stg.users\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.clicks", "columns": null}], "writes": [{"table": "stg.users", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"community_centers\").toPandas()\ndf[[\"cb_year\", \"date_moved_in\"]].to_sql(\"defensespending\", engine, index=False)\n", "labels": {"reads": [{"table": "community_centers", "columns": null}], "writes": [{"table": "defensespending", "columns": ["cb_year", "date_moved_in"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessels\").toPandas()\ndf[[\"total_value_purchased\", \"case_number\"]].to_sql(\"gamedesign\", engine, index=False)\n", "labels": {"reads": [{"table": "vessels", "columns": null}], "writes": [{"table": "gamedesign", "columns": ["total_value_purchased", "case_number"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mines SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO donorgender (accreditation_level, complaint_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "donorgender", "columns": ["accreditation_level", "complaint_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"sector_incidents\")\nsrc.write.insertInto(\"stellar_transactions\", overwrite=True)\n", "labels": {"reads": [{"table": "sector_incidents", "columns": null}], "writes": [{"table": "stellar_transactions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads.member_point SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO player SELECT * FROM legacy\ncur.execute(\"SELECT vehicle_flight_number, productid FROM consumer_preference LIMIT 180\")\n", "labels": {"reads": [{"table": "consumer_preference", "columns": ["vehicle_flight_number", "productid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.state_code > 209).all()\n# src table: wind_energy_projects\nengine.execute(\"INSERT INTO circular_supply_chain_products SELECT * FROM wind_energy_projects\")\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": null}], "writes": [{"table": "circular_supply_chain_products", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO student_addresses SELECT * FROM legacy\ncur.execute(\"SELECT contract_end_date, num_of_audience FROM networkdevices LIMIT 430\")\n", "labels": {"reads": [{"table": "networkdevices", "columns": ["contract_end_date", "num_of_audience"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO warehouses SELECT total_passengers, hourid, country_of_origin FROM tourdifferences WHERE total_passengers > 282\"\n", "labels": {"reads": [{"table": "tourdifferences", "columns": ["total_passengers", "hourid", "country_of_origin"]}], "writes": [{"table": "warehouses", "columns": ["total_passengers", "hourid", "country_of_origin"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table org_climate_finance --target-dir /tmp/land\n", "labels": {"reads": [{"table": "org_climate_finance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table council_tax --columns fair_labor,startup_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "council_tax", "columns": ["fair_labor", "startup_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO climate_adaptation_projects SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO dancefunding SELECT a.total_value_purchased, b.machine_id FROM climate_finance_organizations a JOIN ocean b ON a.inspection_id = b.inspection_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "climate_finance_organizations", "columns": null}, {"table": "ocean", "columns": null}], "writes": [{"table": "dancefunding", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO mart.shipments_full SELECT excavation_site, unavailable, datetime_detention_start FROM status WHERE excavation_site > 8\")\n", "labels": {"reads": [{"table": "status", "columns": ["excavation_site", "unavailable", "datetime_detention_start"]}], "writes": [{"table": "mart.shipments_full", "columns": ["excavation_site", "unavailable", "datetime_detention_start"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model impact_investments depends on animals\ndbt build -s impact_investments --vars '{\"src\":\"animals\"}'\n", "labels": {"reads": [{"table": "animals", "columns": null}], "writes": [{"table": "impact_investments", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.site_name > 487).all()\n# src table: tracks\nengine.execute(\"INSERT INTO broadband_providers SELECT * FROM tracks\")\n", "labels": {"reads": [{"table": "tracks", "columns": null}], "writes": [{"table": "broadband_providers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO media_library SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"government_funding\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "government_funding", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO open_pedagogy_enrollment SELECT a.participant_details, b.diagnosis FROM ocean_acidity a JOIN journal_committee b ON a.institution = b.institution\"\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": null}, {"table": "journal_committee", "columns": null}], "writes": [{"table": "open_pedagogy_enrollment", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mental_health_parity_violations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"market_trends\")\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}], "writes": [{"table": "market_trends", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO inclusivehousing.affordablehousing SELECT facilityid, skill_id, transaction_category FROM nursing_homes WHERE facilityid > 195\"\n", "labels": {"reads": [{"table": "nursing_homes", "columns": ["facilityid", "skill_id", "transaction_category"]}], "writes": [{"table": "inclusivehousing.affordablehousing", "columns": ["facilityid", "skill_id", "transaction_category"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model member_point_daily depends on iron_ore_production\ndbt run -s member_point_daily --vars 'source: iron_ore_production'\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": null}], "writes": [{"table": "member_point_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"producers\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"fireincidents\")\n", "labels": {"reads": [{"table": "producers", "columns": null}], "writes": [{"table": "fireincidents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"peacekeeping_units\")\nsrc.write.insertInto(\"performingartsprograms\", overwrite=True)\n", "labels": {"reads": [{"table": "peacekeeping_units", "columns": null}], "writes": [{"table": "performingartsprograms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"protein\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "protein", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO auto_show SELECT fda_approved, chargeable_amount, draft_pick_number FROM fairness_scores WHERE fda_approved > 199\"\n", "labels": {"reads": [{"table": "fairness_scores", "columns": ["fda_approved", "chargeable_amount", "draft_pick_number"]}], "writes": [{"table": "auto_show", "columns": ["fda_approved", "chargeable_amount", "draft_pick_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO wastewater_treatment (ai_powered_features, disease) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "wastewater_treatment", "columns": ["ai_powered_features", "disease"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO movie SELECT a.therapy_session, b.price_range FROM mart.mart_sessions_di a JOIN digital_divide_initiatives b ON a.amount_of_refund = b.amount_of_refund\"\n", "labels": {"reads": [{"table": "mart.mart_sessions_di", "columns": null}, {"table": "digital_divide_initiatives", "columns": null}], "writes": [{"table": "movie", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO communitypolicing SELECT active_from_date, denomination FROM spending WHERE active_from_date > 106\")\n", "labels": {"reads": [{"table": "spending", "columns": ["active_from_date", "denomination"]}], "writes": [{"table": "communitypolicing", "columns": ["active_from_date", "denomination"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"obesity\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bike_station_info\")\n", "labels": {"reads": [{"table": "obesity", "columns": null}], "writes": [{"table": "bike_station_info", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"online_platform\").toPandas()\ndf[[\"trip_distance\", \"centername\"]].to_sql(\"museums\", engine, index=False)\n", "labels": {"reads": [{"table": "online_platform", "columns": null}], "writes": [{"table": "museums", "columns": ["trip_distance", "centername"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT saleid, date_in_location_from FROM member_details\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"equipment_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "member_details", "columns": ["saleid", "date_in_location_from"]}], "writes": [{"table": "equipment_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO astronaut_medical_3 SELECT a.protected, b.hiredate FROM leo_missions a JOIN fairness_scores b ON a.date_order_placed = b.date_order_placed\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "leo_missions", "columns": null}, {"table": "fairness_scores", "columns": null}], "writes": [{"table": "astronaut_medical_3", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table militarypatents --columns disaster_id,manufacturerid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "militarypatents", "columns": ["disaster_id", "manufacturerid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO school_details SELECT * FROM legacy\ncur.execute(\"SELECT reason, latitude FROM user_reactions LIMIT 172\")\n", "labels": {"reads": [{"table": "user_reactions", "columns": ["reason", "latitude"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model irrigation_systems depends on building\ndbt build --select irrigation_systems --vars 'source: building'\n", "labels": {"reads": [{"table": "building", "columns": null}], "writes": [{"table": "irrigation_systems", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"bioprocess.engineering_projects\")\npush_to_target(df, \"london.stations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": null}], "writes": [{"table": "london.stations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM rural_areas\", conn)\ndf.to_sql(\"check_ins\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "rural_areas", "columns": null}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO nasa_mars_program (approval_date, judge_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "nasa_mars_program", "columns": ["approval_date", "judge_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO accessibility_audits SELECT a.hometeamid, b.review_text FROM oceanography a JOIN research_grants b ON a.labor_cost = b.labor_cost\"\n", "labels": {"reads": [{"table": "oceanography", "columns": null}, {"table": "research_grants", "columns": null}], "writes": [{"table": "accessibility_audits", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM stg.stg_risk_score_hourly\"\n", "labels": {"reads": [{"table": "stg.stg_risk_score_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"runs\")\nsrc.write.insertInto(\"convictions\", overwrite=True)\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": [{"table": "convictions", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO product SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO aircraftsquadrons SELECT implementation_date, fish_population, hotel_name, facility_id FROM carbonoffsetinitiatives WHERE implementation_date > 437\"\n", "labels": {"reads": [{"table": "carbonoffsetinitiatives", "columns": ["implementation_date", "fish_population", "hotel_name", "facility_id"]}], "writes": [{"table": "aircraftsquadrons", "columns": ["implementation_date", "fish_population", "hotel_name", "facility_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"plants\")\nsrc.write.insertInto(\"wind_energy_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "plants", "columns": null}], "writes": [{"table": "wind_energy_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT supplierid, donortype FROM bioprocess_engineering\", engine)\nlogger = logging.getLogger(__name__)\nimport logging\ndf.to_sql(\"canada_cosmetics_preferences\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bioprocess_engineering", "columns": ["supplierid", "donortype"]}], "writes": [{"table": "canada_cosmetics_preferences", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"royal_family\")\nwrite_to_output(df, \"public.police_calls\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "royal_family", "columns": null}], "writes": [{"table": "public.police_calls", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO attendee_demographics (contract_end, clean_jerk) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "attendee_demographics", "columns": ["contract_end", "clean_jerk"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"parts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_orders\")\n", "labels": {"reads": [{"table": "parts", "columns": null}], "writes": [{"table": "ads.ads_orders", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO blockchain_tech SELECT valuation, is_commercial, donortype FROM organic_farms WHERE valuation > 60\"\n", "labels": {"reads": [{"table": "organic_farms", "columns": ["valuation", "is_commercial", "donortype"]}], "writes": [{"table": "blockchain_tech", "columns": ["valuation", "is_commercial", "donortype"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO home_game SELECT position, creationyear FROM funding_records WHERE position > 312\")\n", "labels": {"reads": [{"table": "funding_records", "columns": ["position", "creationyear"]}], "writes": [{"table": "home_game", "columns": ["position", "creationyear"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO marine_life_research SELECT company_type_code, served_subscribers, property_id FROM singer_in_concert WHERE company_type_code > 246\"\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["company_type_code", "served_subscribers", "property_id"]}], "writes": [{"table": "marine_life_research", "columns": ["company_type_code", "served_subscribers", "property_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model clinical_trials depends on carbon_footprint\ndbt build -s clinical_trials --vars '{\"source_table\":\"carbon_footprint\"}'\n", "labels": {"reads": [{"table": "carbon_footprint", "columns": null}], "writes": [{"table": "clinical_trials", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"asia_events\")\nsrc.write.insertInto(\"member_data\", overwrite=True)\n", "labels": {"reads": [{"table": "asia_events", "columns": null}], "writes": [{"table": "member_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO unionnegotiations SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO environmental_impact_stats SELECT course_completion, main_industry, startup_id, restaurant_name FROM sustainable_materials WHERE course_completion > 231;\nEOF\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": ["course_completion", "main_industry", "startup_id", "restaurant_name"]}], "writes": [{"table": "environmental_impact_stats", "columns": ["course_completion", "main_industry", "startup_id", "restaurant_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO chemical_production_3 SELECT a.visitor_count, b.branch FROM urban_transportation a JOIN restaurant_type b ON a.strain_id = b.strain_id\"\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}, {"table": "restaurant_type", "columns": null}], "writes": [{"table": "chemical_production_3", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vendorfabrics\").toPandas()\ndf[[\"participation_date\", \"max_depth\"]].to_sql(\"pilot\", engine, index=False)\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": null}], "writes": [{"table": "pilot", "columns": ["participation_date", "max_depth"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bike_stations SELECT vaccinations, founders_lgbtq, team_id_br, restaurant FROM human_resources WHERE vaccinations > 342\"\n", "labels": {"reads": [{"table": "human_resources", "columns": ["vaccinations", "founders_lgbtq", "team_id_br", "restaurant"]}], "writes": [{"table": "bike_stations", "columns": ["vaccinations", "founders_lgbtq", "team_id_br", "restaurant"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"trucks\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "trucks", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT contactid, trend FROM mine_workforce LIMIT 106;\nINSERT INTO multimodal_trips SELECT booking_date, advisor FROM assets WHERE booking_date > 466;\nSQL\n", "labels": {"reads": [{"table": "mine_workforce", "columns": ["contactid", "trend"]}, {"table": "assets", "columns": ["booking_date", "advisor"]}], "writes": [{"table": "multimodal_trips", "columns": ["booking_date", "advisor"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT fish_id, lettergrade FROM ads.ads_exposure_daily LIMIT 37\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO pacific_ocean SELECT implementation_year, biomass, part_id FROM teacher_development_race WHERE implementation_year > 55\")\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": ["fish_id", "lettergrade"]}, {"table": "teacher_development_race", "columns": ["implementation_year", "biomass", "part_id"]}], "writes": [{"table": "pacific_ocean", "columns": ["implementation_year", "biomass", "part_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"organic_cosmetics\")\nsrc.write.insertInto(\"mining_operation_data\", overwrite=True)\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "mining_operation_data", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO product_categories SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organic_cosmetics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"exoplanet_discoveries\")\n", "labels": {"reads": [{"table": "organic_cosmetics", "columns": null}], "writes": [{"table": "exoplanet_discoveries", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO race (flno, mouse_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "race", "columns": ["flno", "mouse_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dw.dw_inventory_delta SELECT a.outcome_code, b.ingredient FROM socialimpactinvestments a JOIN manufacturing_processes b ON a.paintingid = b.paintingid\"\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}, {"table": "manufacturing_processes", "columns": null}], "writes": [{"table": "dw.dw_inventory_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO military_personnel SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM flights\", conn)\ndf.to_sql(\"traffic_violations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "flights", "columns": null}], "writes": [{"table": "traffic_violations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO employee (party, registration_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "employee", "columns": ["party", "registration_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dailystreams SELECT playdate, permit_id FROM sustainable_fabrics WHERE playdate > 21\"\n", "labels": {"reads": [{"table": "sustainable_fabrics", "columns": ["playdate", "permit_id"]}], "writes": [{"table": "dailystreams", "columns": ["playdate", "permit_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"basketball_teams\")\nsrc.write.insertInto(\"parking_fines\", overwrite=True)\n", "labels": {"reads": [{"table": "basketball_teams", "columns": null}], "writes": [{"table": "parking_fines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table language --columns artist_id,date_incident_end --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "language", "columns": ["artist_id", "date_incident_end"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT method_name, site FROM adrprograms LIMIT 27;\nINSERT INTO organic_farms SELECT languageid, emp_id FROM garmentproduction WHERE languageid > 236;\nSQL\n", "labels": {"reads": [{"table": "adrprograms", "columns": ["method_name", "site"]}, {"table": "garmentproduction", "columns": ["languageid", "emp_id"]}], "writes": [{"table": "organic_farms", "columns": ["languageid", "emp_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_renewable_energy\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"asteroids\")\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": null}], "writes": [{"table": "asteroids", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO digital_assets SELECT artworkname, budget_allocation, left_office, target_u_id FROM claims WHERE artworkname > 20\"], check=True)\n", "labels": {"reads": [{"table": "claims", "columns": ["artworkname", "budget_allocation", "left_office", "target_u_id"]}], "writes": [{"table": "digital_assets", "columns": ["artworkname", "budget_allocation", "left_office", "target_u_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT fault_log_entry_id, initiative_id FROM aircraft LIMIT 385;\nINSERT INTO euroavev SELECT sex, caloric_content, delegate, stu_num FROM bi.bi_payments_delta WHERE sex > 41;\nSQL\n", "labels": {"reads": [{"table": "aircraft", "columns": ["fault_log_entry_id", "initiative_id"]}, {"table": "bi.bi_payments_delta", "columns": ["sex", "caloric_content", "delegate", "stu_num"]}], "writes": [{"table": "euroavev", "columns": ["sex", "caloric_content", "delegate", "stu_num"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"storage\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"biotech_startups\")\n", "labels": {"reads": [{"table": "storage", "columns": null}], "writes": [{"table": "biotech_startups", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO sitem SELECT policy_number, coownerid, unit_price, employee FROM crops_year WHERE policy_number > 335\"\n", "labels": {"reads": [{"table": "crops_year", "columns": ["policy_number", "coownerid", "unit_price", "employee"]}], "writes": [{"table": "sitem", "columns": ["policy_number", "coownerid", "unit_price", "employee"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO aircraft SELECT a.class_code, b.contributorname FROM stg.stg_campaigns_hourly a JOIN haircare_cruelty b ON a.production = b.production\"\n", "labels": {"reads": [{"table": "stg.stg_campaigns_hourly", "columns": null}, {"table": "haircare_cruelty", "columns": null}], "writes": [{"table": "aircraft", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bioprocess.engineering_projects (meter_200, market_rate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bioprocess.engineering_projects", "columns": ["meter_200", "market_rate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM results\"\n", "labels": {"reads": [{"table": "results", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"manufacturing_processes\")\ndump_to_target(df, \"union_membership\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "manufacturing_processes", "columns": null}], "writes": [{"table": "union_membership", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO music_streaming SELECT last_checkup_date, sales_transaction_id, date_valid_from, technique_id FROM orgdonations WHERE last_checkup_date > 9\")\n", "labels": {"reads": [{"table": "orgdonations", "columns": ["last_checkup_date", "sales_transaction_id", "date_valid_from", "technique_id"]}], "writes": [{"table": "music_streaming", "columns": ["last_checkup_date", "sales_transaction_id", "date_valid_from", "technique_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT production_cost, followers FROM asia_events LIMIT 115;\nINSERT INTO dapps SELECT brand, issue_id, cell_mobile_phone_number FROM country_waste_generation WHERE brand > 125;\nSQL\n", "labels": {"reads": [{"table": "asia_events", "columns": ["production_cost", "followers"]}, {"table": "country_waste_generation", "columns": ["brand", "issue_id", "cell_mobile_phone_number"]}], "writes": [{"table": "dapps", "columns": ["brand", "issue_id", "cell_mobile_phone_number"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"victims\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tweets\")\n", "labels": {"reads": [{"table": "victims", "columns": null}], "writes": [{"table": "tweets", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO jobs SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM biosensors\", conn)\ndf.to_sql(\"materials_usage\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "biosensors", "columns": null}], "writes": [{"table": "materials_usage", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table public_transportation_routes --columns device_name,outcome_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "public_transportation_routes", "columns": ["device_name", "outcome_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO healthcare_access_v2 SELECT a.game_count, b.payment_date FROM daily_transaction_volume a JOIN ref_colors b ON a.num_workers = b.num_workers\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "daily_transaction_volume", "columns": null}, {"table": "ref_colors", "columns": null}], "writes": [{"table": "healthcare_access_v2", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.dws_clicks_full SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT founder_count, veteran_unemployment_rate FROM org_volunteer LIMIT 318\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "org_volunteer", "columns": ["founder_count", "veteran_unemployment_rate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO imagery_archive SELECT artist_gender, purchase_transaction_id, fabricid FROM bi.bi_vendors_di WHERE artist_gender > 363\"\n", "labels": {"reads": [{"table": "bi.bi_vendors_di", "columns": ["artist_gender", "purchase_transaction_id", "fabricid"]}], "writes": [{"table": "imagery_archive", "columns": ["artist_gender", "purchase_transaction_id", "fabricid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO country_labor SELECT shipment_year, ingredient_name, crop, serving_size FROM prescribes WHERE shipment_year > 68\"\n", "labels": {"reads": [{"table": "prescribes", "columns": ["shipment_year", "ingredient_name", "crop", "serving_size"]}], "writes": [{"table": "country_labor", "columns": ["shipment_year", "ingredient_name", "crop", "serving_size"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"menuitems\")\nwrite_to_store(df, \"indigenouscommunities\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "menuitems", "columns": null}], "writes": [{"table": "indigenouscommunities", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"arcticocean\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ticketsales\")\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": [{"table": "ticketsales", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM extraction_methods\", conn)\ndf.to_sql(\"haircare_cruelty\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "extraction_methods", "columns": null}], "writes": [{"table": "haircare_cruelty", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT thefttype, client FROM community_policing LIMIT 169\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO labor_statistics SELECT budget_allocated, installation_year, enroll_grade, feedtype FROM tokyo_water_consumption WHERE budget_allocated > 432\")\n", "labels": {"reads": [{"table": "community_policing", "columns": ["thefttype", "client"]}, {"table": "tokyo_water_consumption", "columns": ["budget_allocated", "installation_year", "enroll_grade", "feedtype"]}], "writes": [{"table": "labor_statistics", "columns": ["budget_allocated", "installation_year", "enroll_grade", "feedtype"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"fraud_detections\")\nsave_to_output(df, \"vrusers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fraud_detections", "columns": null}], "writes": [{"table": "vrusers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO coral_reefs SELECT neighborhoodname, catalog_entry_id, projectid FROM shariah_compliant_finance WHERE neighborhoodname > 30\"\n", "labels": {"reads": [{"table": "shariah_compliant_finance", "columns": ["neighborhoodname", "catalog_entry_id", "projectid"]}], "writes": [{"table": "coral_reefs", "columns": ["neighborhoodname", "catalog_entry_id", "projectid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"taj_mahal_visitors\").toPandas()\ndf[[\"high_temperature\", \"building_name\"]].to_sql(\"team\", engine, index=False)\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": null}], "writes": [{"table": "team", "columns": ["high_temperature", "building_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM vessel_incident_count\", conn)\ndf.to_sql(\"cotton_source\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": null}], "writes": [{"table": "cotton_source", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urbanagricrop\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"date\")\n", "labels": {"reads": [{"table": "urbanagricrop", "columns": null}], "writes": [{"table": "date", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO salinity_readings SELECT last_workout_date, capacity_mw, team_id_winner, community_size FROM chemical_processes WHERE last_workout_date > 226\"\n", "labels": {"reads": [{"table": "chemical_processes", "columns": ["last_workout_date", "capacity_mw", "team_id_winner", "community_size"]}], "writes": [{"table": "salinity_readings", "columns": ["last_workout_date", "capacity_mw", "team_id_winner", "community_size"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"enzyme\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"voting_record\")\n", "labels": {"reads": [{"table": "enzyme", "columns": null}], "writes": [{"table": "voting_record", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO biosensors.patents SELECT reported, campusfee, interaction_type, acc_regular_season FROM news_report WHERE reported > 252\"\n", "labels": {"reads": [{"table": "news_report", "columns": ["reported", "campusfee", "interaction_type", "acc_regular_season"]}], "writes": [{"table": "biosensors.patents", "columns": ["reported", "campusfee", "interaction_type", "acc_regular_season"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO chemical_production_3 SELECT a.tree_species, b.budget_amount FROM school a JOIN user b ON a.pricepergram = b.pricepergram\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "school", "columns": null}, {"table": "user", "columns": null}], "writes": [{"table": "chemical_production_3", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO market_trends SELECT virtual_tour_views, neighborhood_id, copy_number FROM initiatives WHERE virtual_tour_views > 249\"\n", "labels": {"reads": [{"table": "initiatives", "columns": ["virtual_tour_views", "neighborhood_id", "copy_number"]}], "writes": [{"table": "market_trends", "columns": ["virtual_tour_views", "neighborhood_id", "copy_number"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO engineer_skills SELECT drought_id, avg_yield FROM safety_records WHERE drought_id > 297\"\n", "labels": {"reads": [{"table": "safety_records", "columns": ["drought_id", "avg_yield"]}], "writes": [{"table": "engineer_skills", "columns": ["drought_id", "avg_yield"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table container_receipts --columns transaction_product,movie --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "container_receipts", "columns": ["transaction_product", "movie"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ads.ads_products_full\"\n", "labels": {"reads": [{"table": "ads.ads_products_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tour_guides SELECT disaster_id, incidentdate, astronautid FROM product_characteristics WHERE disaster_id > 45\"\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["disaster_id", "incidentdate", "astronautid"]}], "writes": [{"table": "tour_guides", "columns": ["disaster_id", "incidentdate", "astronautid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.port_code > 132).all()\n# src table: visitor_statistics\nengine.execute(\"INSERT INTO ads.ads_risk_score_hourly SELECT * FROM visitor_statistics\")\n", "labels": {"reads": [{"table": "visitor_statistics", "columns": null}], "writes": [{"table": "ads.ads_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods_member_point_full SELECT iata, vrdevice FROM fare_segments WHERE iata > 298;\nEOF\n", "labels": {"reads": [{"table": "fare_segments", "columns": ["iata", "vrdevice"]}], "writes": [{"table": "ods_member_point_full", "columns": ["iata", "vrdevice"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"stars\")\ndump_to_sink(df, \"pilot_record\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": [{"table": "pilot_record", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO hotel_revenue (participatedinesports, vesselname) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "hotel_revenue", "columns": ["participatedinesports", "vesselname"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.mart_campaigns_daily --columns fish_population,fine_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.mart_campaigns_daily", "columns": ["fish_population", "fine_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bike_station_info SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO digital_trends SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.mission_count > 295).all()\n# src table: visitor_exhibition\nengine.execute(\"INSERT INTO countryintelligenceops SELECT * FROM visitor_exhibition\")\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": null}], "writes": [{"table": "countryintelligenceops", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shops SELECT film_id, donation_date, clicks FROM department WHERE film_id > 138\"\n", "labels": {"reads": [{"table": "department", "columns": ["film_id", "donation_date", "clicks"]}], "writes": [{"table": "shops", "columns": ["film_id", "donation_date", "clicks"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO skincare_sales SELECT fda_approved, average_age FROM traveler WHERE fda_approved > 5\"\n", "labels": {"reads": [{"table": "traveler", "columns": ["fda_approved", "average_age"]}], "writes": [{"table": "skincare_sales", "columns": ["fda_approved", "average_age"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model video_content depends on climate_monitoring_stations\ndbt build --select video_content --vars '{\"source_table\":\"climate_monitoring_stations\"}'\n", "labels": {"reads": [{"table": "climate_monitoring_stations", "columns": null}], "writes": [{"table": "video_content", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO team_revenue SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO cultural_competency_training SELECT interaction_type, saleid, equipment_id, adults FROM mentalhealthprovider WHERE interaction_type > 66\")\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": ["interaction_type", "saleid", "equipment_id", "adults"]}], "writes": [{"table": "cultural_competency_training", "columns": ["interaction_type", "saleid", "equipment_id", "adults"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dwd.dwd_payments_di\"\n", "labels": {"reads": [{"table": "dwd.dwd_payments_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO marinespeciesobservations SELECT train_number, outcome_description, sensor_id, min_dew_point_f FROM artpieces WHERE train_number > 2;\nEOF\n", "labels": {"reads": [{"table": "artpieces", "columns": ["train_number", "outcome_description", "sensor_id", "min_dew_point_f"]}], "writes": [{"table": "marinespeciesobservations", "columns": ["train_number", "outcome_description", "sensor_id", "min_dew_point_f"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table membership --target-dir /tmp/land\n", "labels": {"reads": [{"table": "membership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bustrips SELECT a.size, b.dish_type FROM organisations a JOIN energy_consumption b ON a.incident_region = b.incident_region\"\n", "labels": {"reads": [{"table": "organisations", "columns": null}, {"table": "energy_consumption", "columns": null}], "writes": [{"table": "bustrips", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table excavationsites --columns faculty_id,customer_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "excavationsites", "columns": ["faculty_id", "customer_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organization_contact_individuals\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"gender\")\n", "labels": {"reads": [{"table": "organization_contact_individuals", "columns": null}], "writes": [{"table": "gender", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO onlineengagement SELECT contributionid, stop, review_id FROM dws_coupon_use_df WHERE contributionid > 482\"\n", "labels": {"reads": [{"table": "dws_coupon_use_df", "columns": ["contributionid", "stop", "review_id"]}], "writes": [{"table": "onlineengagement", "columns": ["contributionid", "stop", "review_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT units_owned, conferenceid FROM product_info LIMIT 407;\nINSERT INTO sales_by_quarter SELECT skill_id, resname, manufacturername FROM public_works_projects WHERE skill_id > 432;\nSQL\n", "labels": {"reads": [{"table": "product_info", "columns": ["units_owned", "conferenceid"]}, {"table": "public_works_projects", "columns": ["skill_id", "resname", "manufacturername"]}], "writes": [{"table": "sales_by_quarter", "columns": ["skill_id", "resname", "manufacturername"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitionsartworks\").toPandas()\ndf[[\"home_team_three_point\", \"startup_id\"]].to_sql(\"disaster_response\", engine, index=False)\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": [{"table": "disaster_response", "columns": ["home_team_three_point", "startup_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT left_office, pets_allowed_yn FROM agricultural_innovation_projects LIMIT 71\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "agricultural_innovation_projects", "columns": ["left_office", "pets_allowed_yn"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO iron_ore_production SELECT last_workout_date, typical_buying_price FROM chemical_processes WHERE last_workout_date > 464\")\n", "labels": {"reads": [{"table": "chemical_processes", "columns": ["last_workout_date", "typical_buying_price"]}], "writes": [{"table": "iron_ore_production", "columns": ["last_workout_date", "typical_buying_price"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO mental_health_parity SELECT building_address, unsure_rate, tier, star_rating_code FROM labor_costs WHERE building_address > 347\"\n", "labels": {"reads": [{"table": "labor_costs", "columns": ["building_address", "unsure_rate", "tier", "star_rating_code"]}], "writes": [{"table": "mental_health_parity", "columns": ["building_address", "unsure_rate", "tier", "star_rating_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO researchgrants SELECT invested, festival_name, individual_name FROM stg.member_point_df WHERE invested > 205\"\n", "labels": {"reads": [{"table": "stg.member_point_df", "columns": ["invested", "festival_name", "individual_name"]}], "writes": [{"table": "researchgrants", "columns": ["invested", "festival_name", "individual_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_initiatives\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"media_types\")\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "media_types", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT rid, manager_name FROM trade_history LIMIT 187\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "trade_history", "columns": ["rid", "manager_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO sfc_articles (allergy, oil_volume) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "sfc_articles", "columns": ["allergy", "oil_volume"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO communityengagements SELECT date_valid_to, assessment_date, amenity_name FROM advisor WHERE date_valid_to > 82\"\n", "labels": {"reads": [{"table": "advisor", "columns": ["date_valid_to", "assessment_date", "amenity_name"]}], "writes": [{"table": "communityengagements", "columns": ["date_valid_to", "assessment_date", "amenity_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.campaigns_full (profits_in_billion, refugee_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.campaigns_full", "columns": ["profits_in_billion", "refugee_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO broadband_plans SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT asset_model, temp FROM donation LIMIT 355\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "donation", "columns": ["asset_model", "temp"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO us_military_personnel SELECT partid, head_id FROM autonomousdriving WHERE partid > 124\"\n", "labels": {"reads": [{"table": "autonomousdriving", "columns": ["partid", "head_id"]}], "writes": [{"table": "us_military_personnel", "columns": ["partid", "head_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"exhibitionsartworks\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"tree_types\")\n", "labels": {"reads": [{"table": "exhibitionsartworks", "columns": null}], "writes": [{"table": "tree_types", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ods_exposure_delta SELECT route_name, amount_paid, project_category, detention_type_code FROM dwd.dwd_campaigns_df WHERE route_name > 358\"\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_df", "columns": ["route_name", "amount_paid", "project_category", "detention_type_code"]}], "writes": [{"table": "ods_exposure_delta", "columns": ["route_name", "amount_paid", "project_category", "detention_type_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO developers SELECT * FROM legacy\ncur.execute(\"SELECT share_in_percent, carrierid FROM music_streaming LIMIT 34\")\n", "labels": {"reads": [{"table": "music_streaming", "columns": ["share_in_percent", "carrierid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"underwater_cables\").toPandas()\ndf[[\"team_id_loser\", \"sale_price\"]].to_sql(\"competition\", engine, index=False)\n", "labels": {"reads": [{"table": "underwater_cables", "columns": null}], "writes": [{"table": "competition", "columns": ["team_id_loser", "sale_price"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO customer_size_diversity SELECT a.address_content, b.forename FROM autoshows a JOIN urbanagricrop b ON a.violation_id = b.violation_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "autoshows", "columns": null}, {"table": "urbanagricrop", "columns": null}], "writes": [{"table": "customer_size_diversity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO user_video_view SELECT inspectionid, workshop_group_id, city_traffic_speed, award FROM funding_rounds WHERE inspectionid > 161\"\n", "labels": {"reads": [{"table": "funding_rounds", "columns": ["inspectionid", "workshop_group_id", "city_traffic_speed", "award"]}], "writes": [{"table": "user_video_view", "columns": ["inspectionid", "workshop_group_id", "city_traffic_speed", "award"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO travel_advisory (water_type, contractorid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "travel_advisory", "columns": ["water_type", "contractorid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.ods_risk_score_df SELECT a.profession, b.origin_city FROM financial_transactions a JOIN chemical_processes b ON a.commodity = b.commodity\"\n", "labels": {"reads": [{"table": "financial_transactions", "columns": null}, {"table": "chemical_processes", "columns": null}], "writes": [{"table": "ods.ods_risk_score_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO ai_safety_incidents SELECT participated_in_open_pedagogy, dishid FROM exit_strategy WHERE participated_in_open_pedagogy > 20\"\n", "labels": {"reads": [{"table": "exit_strategy", "columns": ["participated_in_open_pedagogy", "dishid"]}], "writes": [{"table": "ai_safety_incidents", "columns": ["participated_in_open_pedagogy", "dishid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO mealtypes SELECT a.maintenance_contract_id, b.mhw_id FROM tencel_sources a JOIN performances b ON a.tourist_id = b.tourist_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tencel_sources", "columns": null}, {"table": "performances", "columns": null}], "writes": [{"table": "mealtypes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"record\")\nsrc.write.insertInto(\"indigenous_communities\", overwrite=True)\n", "labels": {"reads": [{"table": "record", "columns": null}], "writes": [{"table": "indigenous_communities", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO attendance SELECT attendanceid, damage_millions_usd, club_name, postal_code FROM tournaments WHERE attendanceid > 141;\nEOF\n", "labels": {"reads": [{"table": "tournaments", "columns": ["attendanceid", "damage_millions_usd", "club_name", "postal_code"]}], "writes": [{"table": "attendance", "columns": ["attendanceid", "damage_millions_usd", "club_name", "postal_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sustainability_certified, participated_in_open_pedagogy FROM bus_routes\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"organization_contact_individuals\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bus_routes", "columns": ["sustainability_certified", "participated_in_open_pedagogy"]}], "writes": [{"table": "organization_contact_individuals", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO airport_aircraft (posted_at, clicks) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "airport_aircraft", "columns": ["posted_at", "clicks"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table parks --target-dir /tmp/land\n", "labels": {"reads": [{"table": "parks", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"seasonalvegetables\")\npush_to_warehouse(df, \"defense_diplomacy\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "seasonalvegetables", "columns": null}], "writes": [{"table": "defense_diplomacy", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainabilityratings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"industry_funding\")\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": null}], "writes": [{"table": "industry_funding", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO legal_aid_providers SELECT a.centername, b.algorithm_name FROM genderdistribution a JOIN city_department b ON a.experience = b.experience\"\n", "labels": {"reads": [{"table": "genderdistribution", "columns": null}, {"table": "city_department", "columns": null}], "writes": [{"table": "legal_aid_providers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO round (framework_id, organic) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "round", "columns": ["framework_id", "organic"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stores\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "stores", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table healthbudget --columns scooter_id,protein_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "healthbudget", "columns": ["scooter_id", "protein_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO performingartsprograms SELECT installation_year, store_name FROM support_groups WHERE installation_year > 112;\nEOF\n", "labels": {"reads": [{"table": "support_groups", "columns": ["installation_year", "store_name"]}], "writes": [{"table": "performingartsprograms", "columns": ["installation_year", "store_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_campaigns_df\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"digital_trends\")\n", "labels": {"reads": [{"table": "dws.dws_campaigns_df", "columns": null}], "writes": [{"table": "digital_trends", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO sustainable_urban_properties_2 SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO biomes SELECT share_in_percent, outcome_type, nominee, donor FROM communication_scores WHERE share_in_percent > 107\"\n", "labels": {"reads": [{"table": "communication_scores", "columns": ["share_in_percent", "outcome_type", "nominee", "donor"]}], "writes": [{"table": "biomes", "columns": ["share_in_percent", "outcome_type", "nominee", "donor"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO contract_negotiations SELECT * FROM legacy\ncur.execute(\"SELECT promotionid, energy_efficiency_kwh_m2_year FROM threat_intelligence LIMIT 472\")\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["promotionid", "energy_efficiency_kwh_m2_year"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT tank, major FROM workout_sessions\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\ndf.to_sql(\"tb_cases\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "workout_sessions", "columns": ["tank", "major"]}], "writes": [{"table": "tb_cases", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tunnels (num_investments, advisoryid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tunnels", "columns": ["num_investments", "advisoryid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"customer_addresses\")\nsrc.write.insertInto(\"tickets\", overwrite=True)\n", "labels": {"reads": [{"table": "customer_addresses", "columns": null}], "writes": [{"table": "tickets", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT permitid, lesson_status_code FROM agroecology_practices LIMIT 291\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO salary SELECT passenger_id, customer_address FROM useracct WHERE passenger_id > 375\")\n", "labels": {"reads": [{"table": "agroecology_practices", "columns": ["permitid", "lesson_status_code"]}, {"table": "useracct", "columns": ["passenger_id", "customer_address"]}], "writes": [{"table": "salary", "columns": ["passenger_id", "customer_address"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM overwatch_scores\"\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO genre SELECT profession, resource FROM bus_routes WHERE profession > 390\"\n", "labels": {"reads": [{"table": "bus_routes", "columns": ["profession", "resource"]}], "writes": [{"table": "genre", "columns": ["profession", "resource"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO farm SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO agri_innov (cropname, school_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "agri_innov", "columns": ["cropname", "school_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO genetics.experiments SELECT zip_code, emp_num, wrestler_id, ad_id FROM mart.mart_campaigns_daily WHERE zip_code > 161\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_campaigns_daily", "columns": ["zip_code", "emp_num", "wrestler_id", "ad_id"]}], "writes": [{"table": "genetics.experiments", "columns": ["zip_code", "emp_num", "wrestler_id", "ad_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO election SELECT a.tree_id, b.exit_strategy FROM operate_company a JOIN labor_practices b ON a.days_held = b.days_held\"\n", "labels": {"reads": [{"table": "operate_company", "columns": null}, {"table": "labor_practices", "columns": null}], "writes": [{"table": "election", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table stg.stg_risk_score_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM subway\"\n", "labels": {"reads": [{"table": "subway", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO canada_cosmetics_preferences SELECT a.sale_id, b.use_date FROM member_details a JOIN players b ON a.statement_details = b.statement_details\"\n", "labels": {"reads": [{"table": "member_details", "columns": null}, {"table": "players", "columns": null}], "writes": [{"table": "canada_cosmetics_preferences", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO landfill_capacity SELECT school_id, truck_details FROM youth_fan_participation WHERE school_id > 230\"\n", "labels": {"reads": [{"table": "youth_fan_participation", "columns": ["school_id", "truck_details"]}], "writes": [{"table": "landfill_capacity", "columns": ["school_id", "truck_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO club SELECT * FROM legacy\ncur.execute(\"SELECT stu_hrs, grantid FROM fair_trade_brands LIMIT 326\")\n", "labels": {"reads": [{"table": "fair_trade_brands", "columns": ["stu_hrs", "grantid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.sales_transaction_id > 97).all()\n# src table: farms\nengine.execute(\"INSERT INTO driver SELECT * FROM farms\")\n", "labels": {"reads": [{"table": "farms", "columns": null}], "writes": [{"table": "driver", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vrgames SELECT a.directed_by, b.game_genre FROM space_agencies_2 a JOIN appliances b ON a.causeid = b.causeid\"\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": null}, {"table": "appliances", "columns": null}], "writes": [{"table": "vrgames", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO deep_sea_species SELECT * FROM legacy\ncur.execute(\"SELECT opponent_id, part_id FROM mining.company LIMIT 472\")\n", "labels": {"reads": [{"table": "mining.company", "columns": ["opponent_id", "part_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_business_partnerships\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hotel_business_partnerships", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO permit SELECT offender_name, capital FROM production_costs WHERE offender_name > 423\"\n", "labels": {"reads": [{"table": "production_costs", "columns": ["offender_name", "capital"]}], "writes": [{"table": "permit", "columns": ["offender_name", "capital"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table bioprocess.engineering_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "bioprocess.engineering_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT centername, hearingdate FROM fault_log LIMIT 84\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "fault_log", "columns": ["centername", "hearingdate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ocean_health_monitor\"\n", "labels": {"reads": [{"table": "ocean_health_monitor", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT premise_id, unionid FROM expenses\", engine)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"environmentalimpact\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "expenses", "columns": ["premise_id", "unionid"]}], "writes": [{"table": "environmentalimpact", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table stellar_transactions --target-dir /tmp/land\n", "labels": {"reads": [{"table": "stellar_transactions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT timestamp, promotiondate FROM low_value_contracts LIMIT 346\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO southchinasea.wells SELECT fault_status, dissolved_oxygen, royal_family_id, ironid FROM tokyo_water_consumption WHERE fault_status > 210\")\n", "labels": {"reads": [{"table": "low_value_contracts", "columns": ["timestamp", "promotiondate"]}, {"table": "tokyo_water_consumption", "columns": ["fault_status", "dissolved_oxygen", "royal_family_id", "ironid"]}], "writes": [{"table": "southchinasea.wells", "columns": ["fault_status", "dissolved_oxygen", "royal_family_id", "ironid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT policy_id, is_hybrid FROM startup_founders LIMIT 252;\nINSERT INTO supportservices SELECT show_times_per_day, treatment_date, successful_cb FROM fieldd_info WHERE show_times_per_day > 26;\nSQL\n", "labels": {"reads": [{"table": "startup_founders", "columns": ["policy_id", "is_hybrid"]}, {"table": "fieldd_info", "columns": ["show_times_per_day", "treatment_date", "successful_cb"]}], "writes": [{"table": "supportservices", "columns": ["show_times_per_day", "treatment_date", "successful_cb"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dws_events_di SELECT do_value, assists FROM policy_feedback WHERE do_value > 40;\nEOF\n", "labels": {"reads": [{"table": "policy_feedback", "columns": ["do_value", "assists"]}], "writes": [{"table": "dws_events_di", "columns": ["do_value", "assists"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT support_rate, ratingdate FROM dws.dws_orders LIMIT 312\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO network_infrastructure SELECT yearadded, fieldid FROM bus_fare_collection WHERE yearadded > 83\")\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": ["support_rate", "ratingdate"]}, {"table": "bus_fare_collection", "columns": ["yearadded", "fieldid"]}], "writes": [{"table": "network_infrastructure", "columns": ["yearadded", "fieldid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO measurements SELECT actor_id, ship_date FROM fossil_fuel_vehicles WHERE actor_id > 92\"\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": ["actor_id", "ship_date"]}], "writes": [{"table": "measurements", "columns": ["actor_id", "ship_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT tree_species, pettype FROM sites_me\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"member_of\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "sites_me", "columns": ["tree_species", "pettype"]}], "writes": [{"table": "member_of", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO defenseprojects SELECT bathroom_count, chemicalid, emission_date FROM soilmoisturedata WHERE bathroom_count > 354\"\n", "labels": {"reads": [{"table": "soilmoisturedata", "columns": ["bathroom_count", "chemicalid", "emission_date"]}], "writes": [{"table": "defenseprojects", "columns": ["bathroom_count", "chemicalid", "emission_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mentalhealthprofessional depends on arcticocean\ndbt build --models mentalhealthprofessional --vars '{\"src\":\"arcticocean\"}'\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": [{"table": "mentalhealthprofessional", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"counties\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "counties", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO course_authors_and_tutors (course_description, employeename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "course_authors_and_tutors", "columns": ["course_description", "employeename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO communityhealthworkerscanada SELECT profession, transaction_date, spill_name FROM creative_ai WHERE profession > 205\"], check=True)\n", "labels": {"reads": [{"table": "creative_ai", "columns": ["profession", "transaction_date", "spill_name"]}], "writes": [{"table": "communityhealthworkerscanada", "columns": ["profession", "transaction_date", "spill_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.bi_orders_hourly SELECT mh_id, amount_of_refund, workerid, category_name FROM state_usage WHERE mh_id > 434\"\n", "labels": {"reads": [{"table": "state_usage", "columns": ["mh_id", "amount_of_refund", "workerid", "category_name"]}], "writes": [{"table": "bi.bi_orders_hourly", "columns": ["mh_id", "amount_of_refund", "workerid", "category_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO epl_teams (restypename, menuitemid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "epl_teams", "columns": ["restypename", "menuitemid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.ads_clicks_delta SELECT wrestler_id, activity, amount FROM energy_efficiency_programs WHERE wrestler_id > 239\"], check=True)\n", "labels": {"reads": [{"table": "energy_efficiency_programs", "columns": ["wrestler_id", "activity", "amount"]}], "writes": [{"table": "ads.ads_clicks_delta", "columns": ["wrestler_id", "activity", "amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mining_operation_data SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stories\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart_events_full\")\n", "labels": {"reads": [{"table": "stories", "columns": null}], "writes": [{"table": "mart_events_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO collectivebargaining SELECT bill_id, culturalcompetency FROM electric_buses WHERE bill_id > 454\"\n", "labels": {"reads": [{"table": "electric_buses", "columns": ["bill_id", "culturalcompetency"]}], "writes": [{"table": "collectivebargaining", "columns": ["bill_id", "culturalcompetency"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO public.collected_fare SELECT a.treasurer_vote, b.community_id FROM militarypatents a JOIN organization_contact_individuals b ON a.no_of_customers = b.no_of_customers\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}, {"table": "organization_contact_individuals", "columns": null}], "writes": [{"table": "public.collected_fare", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO product_characteristics (fate, tournament_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "product_characteristics", "columns": ["fate", "tournament_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ads depends on military_sales\ndbt run --models ads --vars '{\"src\":\"military_sales\"}'\n", "labels": {"reads": [{"table": "military_sales", "columns": null}], "writes": [{"table": "ads", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"world_heritage_sites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_member_point_di\")\n", "labels": {"reads": [{"table": "world_heritage_sites", "columns": null}], "writes": [{"table": "dws.dws_member_point_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"budget_allocations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"yoga\")\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "yoga", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT excavation_site_id, organic_ingredients_percentage FROM permit LIMIT 8\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "permit", "columns": ["excavation_site_id", "organic_ingredients_percentage"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM healthcare_budget\", conn)\ndf.to_sql(\"concert_events\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "healthcare_budget", "columns": null}], "writes": [{"table": "concert_events", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM aircraft_flights\"\n", "labels": {"reads": [{"table": "aircraft_flights", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO wildlife_habitats SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bi_inventory_di depends on agencies\ndbt run --models bi_inventory_di --vars '{\"src\":\"agencies\"}'\n", "labels": {"reads": [{"table": "agencies", "columns": null}], "writes": [{"table": "bi_inventory_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table dw.dw_users_di --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw.dw_users_di", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wastewater_plants\").toPandas()\ndf[[\"total_employees\", \"diagnosis\"]].to_sql(\"vehicle\", engine, index=False)\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": null}], "writes": [{"table": "vehicle", "columns": ["total_employees", "diagnosis"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"production_costs\")\nsrc.write.insertInto(\"parties\", overwrite=True)\n", "labels": {"reads": [{"table": "production_costs", "columns": null}], "writes": [{"table": "parties", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT album, policy_type FROM view_product_availability LIMIT 328\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "view_product_availability", "columns": ["album", "policy_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT port, years_operating FROM debris LIMIT 121\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO employeedata SELECT organic_ingredients_percentage, averagespeed, governor, menu_id FROM mental_health_clinics WHERE organic_ingredients_percentage > 209\")\n", "labels": {"reads": [{"table": "debris", "columns": ["port", "years_operating"]}, {"table": "mental_health_clinics", "columns": ["organic_ingredients_percentage", "averagespeed", "governor", "menu_id"]}], "writes": [{"table": "employeedata", "columns": ["organic_ingredients_percentage", "averagespeed", "governor", "menu_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"securityincidents\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.bi_risk_score_delta\")\n", "labels": {"reads": [{"table": "securityincidents", "columns": null}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads_users_hourly SELECT a.exploited, b.restorative_justice FROM customerorders a JOIN ai_safety_incidents b ON a.team_name = b.team_name\"\n", "labels": {"reads": [{"table": "customerorders", "columns": null}, {"table": "ai_safety_incidents", "columns": null}], "writes": [{"table": "ads_users_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"member_of\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"thefts\")\n", "labels": {"reads": [{"table": "member_of", "columns": null}], "writes": [{"table": "thefts", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO station SELECT period, support_rep_id, brandname, cultural_significance FROM genre_songs WHERE period > 217\"\n", "labels": {"reads": [{"table": "genre_songs", "columns": ["period", "support_rep_id", "brandname", "cultural_significance"]}], "writes": [{"table": "station", "columns": ["period", "support_rep_id", "brandname", "cultural_significance"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT used_kb, pettype FROM mart.shipments_delta\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"market\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": ["used_kb", "pettype"]}], "writes": [{"table": "market", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"race\")\nsrc.write.insertInto(\"mart.mart_device_log\", overwrite=True)\n", "labels": {"reads": [{"table": "race", "columns": null}], "writes": [{"table": "mart.mart_device_log", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table guests --columns min_dew_point_f,entryid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "guests", "columns": ["min_dew_point_f", "entryid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"music\")\nsrc.write.insertInto(\"wind_farms\", overwrite=True)\n", "labels": {"reads": [{"table": "music", "columns": null}], "writes": [{"table": "wind_farms", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.impactid > 34).all()\n# src table: environmentalimpact\nengine.execute(\"INSERT INTO match SELECT * FROM environmentalimpact\")\n", "labels": {"reads": [{"table": "environmentalimpact", "columns": null}], "writes": [{"table": "match", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sites_me SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO project_timeline SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO green_buildings_us SELECT fair_labor, registration_id FROM drug_approvals WHERE fair_labor > 20\"], check=True)\n", "labels": {"reads": [{"table": "drug_approvals", "columns": ["fair_labor", "registration_id"]}], "writes": [{"table": "green_buildings_us", "columns": ["fair_labor", "registration_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO australia_offset_programs SELECT inspection_date, completion_status, donor_country, shippingmethod FROM languagesatrisk WHERE inspection_date > 288\"\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": ["inspection_date", "completion_status", "donor_country", "shippingmethod"]}], "writes": [{"table": "australia_offset_programs", "columns": ["inspection_date", "completion_status", "donor_country", "shippingmethod"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO artifactanalysis (invoice_date, num_of_component) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "artifactanalysis", "columns": ["invoice_date", "num_of_component"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model suburbs depends on fan_purchases\ndbt build --models suburbs --vars '{\"source_table\":\"fan_purchases\"}'\n", "labels": {"reads": [{"table": "fan_purchases", "columns": null}], "writes": [{"table": "suburbs", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"container\")\nsrc.write.insertInto(\"livestock\", overwrite=True)\n", "labels": {"reads": [{"table": "container", "columns": null}], "writes": [{"table": "livestock", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT visitor_id, peakhourid FROM waste\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"player_sessions\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "waste", "columns": ["visitor_id", "peakhourid"]}], "writes": [{"table": "player_sessions", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO safety_records SELECT a.is_ev, b.supplier_id FROM sitem a JOIN renewableenergyprojects b ON a.theftid = b.theftid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sitem", "columns": null}, {"table": "renewableenergyprojects", "columns": null}], "writes": [{"table": "safety_records", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO hotel_chains SELECT innovation, item_id, role_name FROM restorative_justice_programs WHERE innovation > 235\"\n", "labels": {"reads": [{"table": "restorative_justice_programs", "columns": ["innovation", "item_id", "role_name"]}], "writes": [{"table": "hotel_chains", "columns": ["innovation", "item_id", "role_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO songs SELECT a.goal_date, b.categoryid FROM exit_strategy a JOIN patient b ON a.membergender = b.membergender\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "exit_strategy", "columns": null}, {"table": "patient", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO settlements SELECT professional_development, galleryid, threat_type, shipmenttype FROM threat_intelligence WHERE professional_development > 42\"\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["professional_development", "galleryid", "threat_type", "shipmenttype"]}], "writes": [{"table": "settlements", "columns": ["professional_development", "galleryid", "threat_type", "shipmenttype"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO ods.inventory_df SELECT assets, program_id, stu_fname, share_in_percent FROM campaigns WHERE assets > 32\"\n", "labels": {"reads": [{"table": "campaigns", "columns": ["assets", "program_id", "stu_fname", "share_in_percent"]}], "writes": [{"table": "ods.inventory_df", "columns": ["assets", "program_id", "stu_fname", "share_in_percent"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"genetics.projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "genetics.projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO satellite_deployment SELECT crop_name, enrollment_date FROM ods_payments_delta WHERE crop_name > 193\"], check=True)\n", "labels": {"reads": [{"table": "ods_payments_delta", "columns": ["crop_name", "enrollment_date"]}], "writes": [{"table": "satellite_deployment", "columns": ["crop_name", "enrollment_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mobile_usage SELECT fair_labor, region FROM erc20_transactions WHERE fair_labor > 99\"], check=True)\n", "labels": {"reads": [{"table": "erc20_transactions", "columns": ["fair_labor", "region"]}], "writes": [{"table": "mobile_usage", "columns": ["fair_labor", "region"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO communityengagementmetrics SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO department_publications SELECT cmi_cross_ref_id, aircraft, owner, training_name FROM therapy_session WHERE cmi_cross_ref_id > 79;\nEOF\n", "labels": {"reads": [{"table": "therapy_session", "columns": ["cmi_cross_ref_id", "aircraft", "owner", "training_name"]}], "writes": [{"table": "department_publications", "columns": ["cmi_cross_ref_id", "aircraft", "owner", "training_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO daily_oil_production SELECT fleet_series, aircraft FROM ai_systems WHERE fleet_series > 158;\nEOF\n", "labels": {"reads": [{"table": "ai_systems", "columns": ["fleet_series", "aircraft"]}], "writes": [{"table": "daily_oil_production", "columns": ["fleet_series", "aircraft"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO co2_sequestration (founding_location, pettype) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "co2_sequestration", "columns": ["founding_location", "pettype"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT virtual_tour_sessions, trench_name FROM dw.inventory_delta LIMIT 135\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "dw.inventory_delta", "columns": ["virtual_tour_sessions", "trench_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"packages\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"document_functional_areas\")\n", "labels": {"reads": [{"table": "packages", "columns": null}], "writes": [{"table": "document_functional_areas", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO nomination (item_type, make) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "nomination", "columns": ["item_type", "make"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shipping_agent_code, is_electric FROM stg.stg_device_log_daily LIMIT 420\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "stg.stg_device_log_daily", "columns": ["shipping_agent_code", "is_electric"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investors\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artifact_analysis\")\n", "labels": {"reads": [{"table": "investors", "columns": null}], "writes": [{"table": "artifact_analysis", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.movie > 343).all()\n# src table: inclusivehousing.affordablehousing\nengine.execute(\"INSERT INTO contracts SELECT * FROM inclusivehousing.affordablehousing\")\n", "labels": {"reads": [{"table": "inclusivehousing.affordablehousing", "columns": null}], "writes": [{"table": "contracts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bioreactor --columns shelter_id,annual_interchanges --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bioreactor", "columns": ["shelter_id", "annual_interchanges"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vehicle_safety_testing SELECT impact_id, num_of_staff FROM military_expenditure WHERE impact_id > 241\"\n", "labels": {"reads": [{"table": "military_expenditure", "columns": ["impact_id", "num_of_staff"]}], "writes": [{"table": "vehicle_safety_testing", "columns": ["impact_id", "num_of_staff"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vrusers SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT host_country, eco_friendly FROM drought_impact LIMIT 122\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "drought_impact", "columns": ["host_country", "eco_friendly"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT gas_production_2020, state_code FROM arctic_marine_species\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"dw.exposure_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "arctic_marine_species", "columns": ["gas_production_2020", "state_code"]}], "writes": [{"table": "dw.exposure_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO defense_project_timelines (offset_id, yearadded) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "defense_project_timelines", "columns": ["offset_id", "yearadded"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO militaryequipmentsales SELECT name_full, sales_id FROM investment_rounds WHERE name_full > 26;\nEOF\n", "labels": {"reads": [{"table": "investment_rounds", "columns": ["name_full", "sales_id"]}], "writes": [{"table": "militaryequipmentsales", "columns": ["name_full", "sales_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"safety_data\")\nsrc.write.insertInto(\"bi.bi_member_point\", overwrite=True)\n", "labels": {"reads": [{"table": "safety_data", "columns": null}], "writes": [{"table": "bi.bi_member_point", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_agencies_2\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"clothingitems\")\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": null}], "writes": [{"table": "clothingitems", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model electricvehicleadoption depends on divisions\ndbt run --select electricvehicleadoption --vars 'source: divisions'\n", "labels": {"reads": [{"table": "divisions", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM shop\"\n", "labels": {"reads": [{"table": "shop", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT mine_id, crs_credit FROM rural_areas LIMIT 108;\nINSERT INTO eu_humanitarian_assistance SELECT call_id, stream_id, weight, stocking_density FROM tourism_activities WHERE call_id > 32;\nSQL\n", "labels": {"reads": [{"table": "rural_areas", "columns": ["mine_id", "crs_credit"]}, {"table": "tourism_activities", "columns": ["call_id", "stream_id", "weight", "stocking_density"]}], "writes": [{"table": "eu_humanitarian_assistance", "columns": ["call_id", "stream_id", "weight", "stocking_density"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model veteran_employment depends on albums\ndbt build -s veteran_employment --vars 'source: albums'\n", "labels": {"reads": [{"table": "albums", "columns": null}], "writes": [{"table": "veteran_employment", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dws_cart_item --columns scoreid,available_yn --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dws_cart_item", "columns": ["scoreid", "available_yn"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.investors > 180).all()\n# src table: financialwellbeing\nengine.execute(\"INSERT INTO companies_extended SELECT * FROM financialwellbeing\")\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": null}], "writes": [{"table": "companies_extended", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table customer --target-dir /tmp/land\n", "labels": {"reads": [{"table": "customer", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT job_title, conservation_status FROM sfc_articles LIMIT 169;\nINSERT INTO ship SELECT i_id, baseprice, date_of_attendance FROM worker_scores WHERE i_id > 488;\nSQL\n", "labels": {"reads": [{"table": "sfc_articles", "columns": ["job_title", "conservation_status"]}, {"table": "worker_scores", "columns": ["i_id", "baseprice", "date_of_attendance"]}], "writes": [{"table": "ship", "columns": ["i_id", "baseprice", "date_of_attendance"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT songid, billing_country FROM militaryoperations LIMIT 64\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ocean_floor_depth SELECT playername, applicant, sales_billion FROM rainfall_data WHERE playername > 59\")\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["songid", "billing_country"]}, {"table": "rainfall_data", "columns": ["playername", "applicant", "sales_billion"]}], "writes": [{"table": "ocean_floor_depth", "columns": ["playername", "applicant", "sales_billion"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM phishing_targets\", conn)\ndf.to_sql(\"trends_2022\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "phishing_targets", "columns": null}], "writes": [{"table": "trends_2022", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO volunteerhours SELECT * FROM legacy\ncur.execute(\"SELECT veteran_unemployment_rate, heritage_site FROM attorney_billing_rates LIMIT 252\")\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": ["veteran_unemployment_rate", "heritage_site"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"org_volunteer\")\nsink_to_target(df, \"atlantic_plate\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "org_volunteer", "columns": null}], "writes": [{"table": "atlantic_plate", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO algorithmic_fairness_incidents_monthly (vegetable, initiative_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "algorithmic_fairness_incidents_monthly", "columns": ["vegetable", "initiative_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"productsafety\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"infra_diversification\")\n", "labels": {"reads": [{"table": "productsafety", "columns": null}], "writes": [{"table": "infra_diversification", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO judges SELECT mean_visibility_miles, fault_short_name, worker_id, city_code FROM ods.sessions WHERE mean_visibility_miles > 33\"\n", "labels": {"reads": [{"table": "ods.sessions", "columns": ["mean_visibility_miles", "fault_short_name", "worker_id", "city_code"]}], "writes": [{"table": "judges", "columns": ["mean_visibility_miles", "fault_short_name", "worker_id", "city_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"distributors\")\nsink_to_sink(df, \"state_budget\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "distributors", "columns": null}], "writes": [{"table": "state_budget", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM spacemissions\", conn)\ndf.to_sql(\"virtual_tours_oceania\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "spacemissions", "columns": null}], "writes": [{"table": "virtual_tours_oceania", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cinema --columns production_usage,animal_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cinema", "columns": ["production_usage", "animal_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO socialimpactinvestments SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table agri_innovations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "agri_innovations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT topic, digital FROM factories LIMIT 456;\nINSERT INTO ods_shipments_df SELECT born_state, neighborhood_id, fare_amount, billing FROM ratings WHERE born_state > 347;\nSQL\n", "labels": {"reads": [{"table": "factories", "columns": ["topic", "digital"]}, {"table": "ratings", "columns": ["born_state", "neighborhood_id", "fare_amount", "billing"]}], "writes": [{"table": "ods_shipments_df", "columns": ["born_state", "neighborhood_id", "fare_amount", "billing"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO exam_results SELECT shipment_date, astronaut_name FROM product_details WHERE shipment_date > 110\")\n", "labels": {"reads": [{"table": "product_details", "columns": ["shipment_date", "astronaut_name"]}], "writes": [{"table": "exam_results", "columns": ["shipment_date", "astronaut_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model al_jazeera_data depends on supply_chain\ndbt run --models al_jazeera_data --vars '{\"src\":\"supply_chain\"}'\n", "labels": {"reads": [{"table": "supply_chain", "columns": null}], "writes": [{"table": "al_jazeera_data", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT policyid, end_date FROM autoshow LIMIT 495;\nINSERT INTO marine_species_arctic_ocean SELECT gametype, school_id, model_name FROM volume WHERE gametype > 412;\nSQL\n", "labels": {"reads": [{"table": "autoshow", "columns": ["policyid", "end_date"]}, {"table": "volume", "columns": ["gametype", "school_id", "model_name"]}], "writes": [{"table": "marine_species_arctic_ocean", "columns": ["gametype", "school_id", "model_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"complaints\")\nupsert_to_store(df, \"ref_product_categories\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "complaints", "columns": null}], "writes": [{"table": "ref_product_categories", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model protein depends on territory.human_rights_data\ndbt run --models protein --vars '{\"src\":\"territory.human_rights_data\"}'\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": null}], "writes": [{"table": "protein", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT day_number, election_cycle FROM textile_waste LIMIT 483;\nINSERT INTO dwd.dwd_users_hourly SELECT workout_duration, model_name FROM fossil_fuel_vehicles WHERE workout_duration > 476;\nSQL\n", "labels": {"reads": [{"table": "textile_waste", "columns": ["day_number", "election_cycle"]}, {"table": "fossil_fuel_vehicles", "columns": ["workout_duration", "model_name"]}], "writes": [{"table": "dwd.dwd_users_hourly", "columns": ["workout_duration", "model_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sqft, competition_type FROM train_lines LIMIT 31;\nINSERT INTO artifactanalysis SELECT vessel_id, strain_id, tree_species, streamid FROM food_items WHERE vessel_id > 446;\nSQL\n", "labels": {"reads": [{"table": "train_lines", "columns": ["sqft", "competition_type"]}, {"table": "food_items", "columns": ["vessel_id", "strain_id", "tree_species", "streamid"]}], "writes": [{"table": "artifactanalysis", "columns": ["vessel_id", "strain_id", "tree_species", "streamid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT machine_series, inventor_name FROM trips LIMIT 370;\nINSERT INTO bike_station_info SELECT is_unionized, observation_date FROM textile_suppliers WHERE is_unionized > 257;\nSQL\n", "labels": {"reads": [{"table": "trips", "columns": ["machine_series", "inventor_name"]}, {"table": "textile_suppliers", "columns": ["is_unionized", "observation_date"]}], "writes": [{"table": "bike_station_info", "columns": ["is_unionized", "observation_date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM researchgrants\"\n", "labels": {"reads": [{"table": "researchgrants", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT submission_id, crime_date FROM ticket_sales LIMIT 454\")\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO part_faults SELECT circuitid, amountdonated, dept_id FROM school_districts WHERE circuitid > 102\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": ["submission_id", "crime_date"]}, {"table": "school_districts", "columns": ["circuitid", "amountdonated", "dept_id"]}], "writes": [{"table": "part_faults", "columns": ["circuitid", "amountdonated", "dept_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.dwd_risk_score_delta (socially_responsible, restock_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_risk_score_delta", "columns": ["socially_responsible", "restock_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO nutrition_facts SELECT entryid, flno, leadershiptraining FROM visitor_exhibition WHERE entryid > 130;\nEOF\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": ["entryid", "flno", "leadershiptraining"]}], "writes": [{"table": "nutrition_facts", "columns": ["entryid", "flno", "leadershiptraining"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT engagement_count, accreditation_level FROM city LIMIT 58\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO culturalcompetencytrainings SELECT vessel_id, first_donation_date FROM material WHERE vessel_id > 459\")\n", "labels": {"reads": [{"table": "city", "columns": ["engagement_count", "accreditation_level"]}, {"table": "material", "columns": ["vessel_id", "first_donation_date"]}], "writes": [{"table": "culturalcompetencytrainings", "columns": ["vessel_id", "first_donation_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.order_item_id > 98).all()\n# src table: mart_orders_di\nengine.execute(\"INSERT INTO circular_economy_companies SELECT * FROM mart_orders_di\")\n", "labels": {"reads": [{"table": "mart_orders_di", "columns": null}], "writes": [{"table": "circular_economy_companies", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table marine_mammals --columns effort_name,mediatorid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "marine_mammals", "columns": ["effort_name", "mediatorid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO esportsteamsafrica (incident_name, document_status_description) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "esportsteamsafrica", "columns": ["incident_name", "document_status_description"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO league_x SELECT concert_id, sale_amount, fate, online_dispute_resolution FROM asia_events WHERE concert_id > 273\")\n", "labels": {"reads": [{"table": "asia_events", "columns": ["concert_id", "sale_amount", "fate", "online_dispute_resolution"]}], "writes": [{"table": "league_x", "columns": ["concert_id", "sale_amount", "fate", "online_dispute_resolution"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO incidents (operationname, color_description) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "incidents", "columns": ["operationname", "color_description"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vehicle_safety_testing SELECT disaster_type, ll_activity FROM fossil_fuel_vehicles WHERE disaster_type > 305\"\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": ["disaster_type", "ll_activity"]}], "writes": [{"table": "vehicle_safety_testing", "columns": ["disaster_type", "ll_activity"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ocean_pollution SELECT a.living_wage, b.initiative_type FROM space_agencies_2 a JOIN military_personnel_africa b ON a.mission_name = b.mission_name\"\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": null}, {"table": "military_personnel_africa", "columns": null}], "writes": [{"table": "ocean_pollution", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO permit SELECT showid, completed_course, engagementid, attack_count FROM pacific_ocean WHERE showid > 242;\nEOF\n", "labels": {"reads": [{"table": "pacific_ocean", "columns": ["showid", "completed_course", "engagementid", "attack_count"]}], "writes": [{"table": "permit", "columns": ["showid", "completed_course", "engagementid", "attack_count"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"city_properties\")\nsrc.write.insertInto(\"ads.ads_refunds_hourly\", overwrite=True)\n", "labels": {"reads": [{"table": "city_properties", "columns": null}], "writes": [{"table": "ads.ads_refunds_hourly", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM player_attributes\"\n", "labels": {"reads": [{"table": "player_attributes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fare_amount, assessmentname FROM consumer\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\ndf.to_sql(\"organizations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "consumer", "columns": ["fare_amount", "assessmentname"]}], "writes": [{"table": "organizations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climateresearch SELECT support_rate, bikes_available, has_aloe_vera FROM weapons WHERE support_rate > 66\"\n", "labels": {"reads": [{"table": "weapons", "columns": ["support_rate", "bikes_available", "has_aloe_vera"]}], "writes": [{"table": "climateresearch", "columns": ["support_rate", "bikes_available", "has_aloe_vera"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO defense_diplomacy SELECT condition, company_name, jan FROM fabrics WHERE condition > 38\"\n", "labels": {"reads": [{"table": "fabrics", "columns": ["condition", "company_name", "jan"]}], "writes": [{"table": "defense_diplomacy", "columns": ["condition", "company_name", "jan"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO wta_serves SELECT school_type, pilot_id, lesson_id, languages FROM water_distribution WHERE school_type > 85\"\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["school_type", "pilot_id", "lesson_id", "languages"]}], "writes": [{"table": "wta_serves", "columns": ["school_type", "pilot_id", "lesson_id", "languages"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO staff_roles SELECT donator_name, route_short_name, special_features FROM student WHERE donator_name > 490\"\n", "labels": {"reads": [{"table": "student", "columns": ["donator_name", "route_short_name", "special_features"]}], "writes": [{"table": "staff_roles", "columns": ["donator_name", "route_short_name", "special_features"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO soilmoisturedata SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO temperaturerecords SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO wholesale_orders (disability, trackid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "wholesale_orders", "columns": ["disability", "trackid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"chemicals_annual\")\nexport_to_store(df, \"invoice\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": null}], "writes": [{"table": "invoice", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO mart.mart_users_df SELECT a.model_id, b.nickname FROM sustainability_metrics a JOIN students_enrollment b ON a.implementation_date = b.implementation_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": null}, {"table": "students_enrollment", "columns": null}], "writes": [{"table": "mart.mart_users_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_users_df\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.mart_users_df", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"dwd.dwd_products\")\nwrite_to_sink(df, \"dw.dw_risk_score_full\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": null}], "writes": [{"table": "dw.dw_risk_score_full", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO aid_missions SELECT matchid, daily_visitors, seal_species FROM submission WHERE matchid > 290;\nEOF\n", "labels": {"reads": [{"table": "submission", "columns": ["matchid", "daily_visitors", "seal_species"]}], "writes": [{"table": "aid_missions", "columns": ["matchid", "daily_visitors", "seal_species"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shark_biomass (warehouseid, schedule_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shark_biomass", "columns": ["warehouseid", "schedule_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO stores_2 SELECT staff_details, seal_species FROM bi.bi_shipments WHERE staff_details > 395\"\n", "labels": {"reads": [{"table": "bi.bi_shipments", "columns": ["staff_details", "seal_species"]}], "writes": [{"table": "stores_2", "columns": ["staff_details", "seal_species"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT element_id, schedule_id FROM ytterbium_supply\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"biosensor.patents\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ytterbium_supply", "columns": ["element_id", "schedule_id"]}], "writes": [{"table": "biosensor.patents", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT has_access, instid FROM production LIMIT 209;\nINSERT INTO developers SELECT cell_mobile_phone_number, case_burden FROM farms WHERE cell_mobile_phone_number > 486;\nSQL\n", "labels": {"reads": [{"table": "production", "columns": ["has_access", "instid"]}, {"table": "farms", "columns": ["cell_mobile_phone_number", "case_burden"]}], "writes": [{"table": "developers", "columns": ["cell_mobile_phone_number", "case_burden"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_source(ctx, \"materials_usage\")\nsave_to_warehouse(df, \"completed_training\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "materials_usage", "columns": null}], "writes": [{"table": "completed_training", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO trainers SELECT strain_type, expertise, strategy_id, prereq_id FROM soil_moisture WHERE strain_type > 399\"], check=True)\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["strain_type", "expertise", "strategy_id", "prereq_id"]}], "writes": [{"table": "trainers", "columns": ["strain_type", "expertise", "strategy_id", "prereq_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mediators\")\nsrc.write.insertInto(\"dws.dws_member_point_di\", overwrite=True)\n", "labels": {"reads": [{"table": "mediators", "columns": null}], "writes": [{"table": "dws.dws_member_point_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vessel SELECT sighting_date, visitorid FROM mentalhealthprofessional WHERE sighting_date > 245\"\n", "labels": {"reads": [{"table": "mentalhealthprofessional", "columns": ["sighting_date", "visitorid"]}], "writes": [{"table": "vessel", "columns": ["sighting_date", "visitorid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO workerbuildings SELECT watertemp, birth_place FROM agricultural_projects WHERE watertemp > 107\"\n", "labels": {"reads": [{"table": "agricultural_projects", "columns": ["watertemp", "birth_place"]}], "writes": [{"table": "workerbuildings", "columns": ["watertemp", "birth_place"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"orders\")\nsrc.write.insertInto(\"virtual_tours_oceania\", overwrite=True)\n", "labels": {"reads": [{"table": "orders", "columns": null}], "writes": [{"table": "virtual_tours_oceania", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT velocity, crime_id FROM community_leaders LIMIT 152\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "community_leaders", "columns": ["velocity", "crime_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model electricvehicleadoption depends on trainingprograms\ndbt run --models electricvehicleadoption --vars '{\"src\":\"trainingprograms\"}'\n", "labels": {"reads": [{"table": "trainingprograms", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dapps SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO irrigation_systems SELECT priceid, dose, season FROM exam_results WHERE priceid > 5\"], check=True)\n", "labels": {"reads": [{"table": "exam_results", "columns": ["priceid", "dose", "season"]}], "writes": [{"table": "irrigation_systems", "columns": ["priceid", "dose", "season"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"support_programs\").toPandas()\ndf[[\"elevation\", \"material_name\"]].to_sql(\"ca_menu_items\", engine, index=False)\n", "labels": {"reads": [{"table": "support_programs", "columns": null}], "writes": [{"table": "ca_menu_items", "columns": ["elevation", "material_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"port_visits\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "port_visits", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mammals SELECT store_name, furniture_id, shipment_year, dose FROM fish_purchases WHERE store_name > 202\"], check=True)\n", "labels": {"reads": [{"table": "fish_purchases", "columns": ["store_name", "furniture_id", "shipment_year", "dose"]}], "writes": [{"table": "mammals", "columns": ["store_name", "furniture_id", "shipment_year", "dose"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donortype, lanes FROM residents_services LIMIT 7\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "residents_services", "columns": ["donortype", "lanes"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.ods_risk_score_df SELECT granteeid, member_id FROM landfill_capacity WHERE granteeid > 454;\nEOF\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": ["granteeid", "member_id"]}], "writes": [{"table": "ods.ods_risk_score_df", "columns": ["granteeid", "member_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO pets SELECT volume_id, bus_number FROM ods_products_delta WHERE volume_id > 482\"\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": ["volume_id", "bus_number"]}], "writes": [{"table": "pets", "columns": ["volume_id", "bus_number"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT occupation, market_details FROM device_accessibility LIMIT 362;\nINSERT INTO ref_locations SELECT condition, word_count, sid, genreid FROM public_works_projects WHERE condition > 496;\nSQL\n", "labels": {"reads": [{"table": "device_accessibility", "columns": ["occupation", "market_details"]}, {"table": "public_works_projects", "columns": ["condition", "word_count", "sid", "genreid"]}], "writes": [{"table": "ref_locations", "columns": ["condition", "word_count", "sid", "genreid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO road_construction SELECT manufacturername, date_joined_staff, measurement FROM stg.stg_risk_score_df WHERE manufacturername > 322\"\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": ["manufacturername", "date_joined_staff", "measurement"]}], "writes": [{"table": "road_construction", "columns": ["manufacturername", "date_joined_staff", "measurement"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table auto_shows --target-dir /tmp/land\n", "labels": {"reads": [{"table": "auto_shows", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO user_stats SELECT people_id, spending, signup_date, operationid FROM dws.shipments_daily WHERE people_id > 75\"\n", "labels": {"reads": [{"table": "dws.shipments_daily", "columns": ["people_id", "spending", "signup_date", "operationid"]}], "writes": [{"table": "user_stats", "columns": ["people_id", "spending", "signup_date", "operationid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO foodaid SELECT a.individual_middle_name, b.vaccination_status FROM collective_bargaining a JOIN virtual_tours b ON a.product_type = b.product_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "collective_bargaining", "columns": null}, {"table": "virtual_tours", "columns": null}], "writes": [{"table": "foodaid", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table marine_life_research --columns trade_name,status_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "marine_life_research", "columns": ["trade_name", "status_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"astronaut_missions\").toPandas()\ndf[[\"payment_method\", \"sale_price\"]].to_sql(\"vrusers\", engine, index=False)\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": null}], "writes": [{"table": "vrusers", "columns": ["payment_method", "sale_price"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table military_equipment_maintenance --columns enable_dm,testdate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "military_equipment_maintenance", "columns": ["enable_dm", "testdate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"virtual_tourism\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"opioid_overdoses\")\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": null}], "writes": [{"table": "opioid_overdoses", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table africa_schema.african_mines --columns added_date,negotiation_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "africa_schema.african_mines", "columns": ["added_date", "negotiation_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO tour_guides SELECT profits_billion, investment_round, effort_id FROM spacecraft_components WHERE profits_billion > 415\")\n", "labels": {"reads": [{"table": "spacecraft_components", "columns": ["profits_billion", "investment_round", "effort_id"]}], "writes": [{"table": "tour_guides", "columns": ["profits_billion", "investment_round", "effort_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO coralreefs SELECT a.representative_name, b.account_name FROM climate_adaptation a JOIN tennis_players b ON a.workshop_group_id = b.workshop_group_id\"\n", "labels": {"reads": [{"table": "climate_adaptation", "columns": null}, {"table": "tennis_players", "columns": null}], "writes": [{"table": "coralreefs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"event_attendance\").toPandas()\ndf[[\"outcome_name\", \"model_name\"]].to_sql(\"trafficviolations\", engine, index=False)\n", "labels": {"reads": [{"table": "event_attendance", "columns": null}], "writes": [{"table": "trafficviolations", "columns": ["outcome_name", "model_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"container_ships\")\nsrc.write.insertInto(\"assessment_notes\", overwrite=True)\n", "labels": {"reads": [{"table": "container_ships", "columns": null}], "writes": [{"table": "assessment_notes", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO atlantic_plate SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO african_tourism SELECT menuname, market_value_in_billion, farmname FROM financial_capability_id WHERE menuname > 337\")\n", "labels": {"reads": [{"table": "financial_capability_id", "columns": ["menuname", "market_value_in_billion", "farmname"]}], "writes": [{"table": "african_tourism", "columns": ["menuname", "market_value_in_billion", "farmname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO jupiter_spacecraft SELECT exoplanet, poll_source FROM mexico_regions WHERE exoplanet > 95\"], check=True)\n", "labels": {"reads": [{"table": "mexico_regions", "columns": ["exoplanet", "poll_source"]}], "writes": [{"table": "jupiter_spacecraft", "columns": ["exoplanet", "poll_source"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT starting_year, trial_status FROM schoolc LIMIT 208;\nINSERT INTO container_receipts SELECT fouls, destination, chemical_id FROM member_data WHERE fouls > 253;\nSQL\n", "labels": {"reads": [{"table": "schoolc", "columns": ["starting_year", "trial_status"]}, {"table": "member_data", "columns": ["fouls", "destination", "chemical_id"]}], "writes": [{"table": "container_receipts", "columns": ["fouls", "destination", "chemical_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_sessions_hourly\", conn)\ndf.to_sql(\"check_ins\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_sessions_hourly", "columns": null}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO attack_outcomes (team_name, sustainability_score) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "attack_outcomes", "columns": ["team_name", "sustainability_score"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO customer_master_index SELECT * FROM legacy\ncur.execute(\"SELECT bname, trend_id FROM training_programs LIMIT 381\")\n", "labels": {"reads": [{"table": "training_programs", "columns": ["bname", "trend_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods_clicks_df SELECT * FROM legacy\ncur.execute(\"SELECT scores, tasktype FROM dw.dw_campaigns_di LIMIT 349\")\n", "labels": {"reads": [{"table": "dw.dw_campaigns_di", "columns": ["scores", "tasktype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table satellites_in_orbit --target-dir /tmp/land\n", "labels": {"reads": [{"table": "satellites_in_orbit", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"animal_species\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"therapy_sessions\")\n", "labels": {"reads": [{"table": "animal_species", "columns": null}], "writes": [{"table": "therapy_sessions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO pollutionincidents SELECT expertise, billid, attorney_id FROM sustainable_materials WHERE expertise > 356;\nEOF\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": ["expertise", "billid", "attorney_id"]}], "writes": [{"table": "pollutionincidents", "columns": ["expertise", "billid", "attorney_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO cybersecuritybudget SELECT a.practice_id, b.filingdate FROM communities a JOIN seeds b ON a.society = b.society\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "communities", "columns": null}, {"table": "seeds", "columns": null}], "writes": [{"table": "cybersecuritybudget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO college SELECT case_burden, artwork, role, has_parabens FROM space_exploration WHERE case_burden > 489\"\n", "labels": {"reads": [{"table": "space_exploration", "columns": ["case_burden", "artwork", "role", "has_parabens"]}], "writes": [{"table": "college", "columns": ["case_burden", "artwork", "role", "has_parabens"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO defense_diplomacy SELECT stockid, problem_description, is_hybrid FROM fish_biomass WHERE stockid > 240\"\n", "labels": {"reads": [{"table": "fish_biomass", "columns": ["stockid", "problem_description", "is_hybrid"]}], "writes": [{"table": "defense_diplomacy", "columns": ["stockid", "problem_description", "is_hybrid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"continent\").toPandas()\ndf[[\"mealname\", \"party\"]].to_sql(\"restaurants\", engine, index=False)\n", "labels": {"reads": [{"table": "continent", "columns": null}], "writes": [{"table": "restaurants", "columns": ["mealname", "party"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT match_id, outcome_name FROM player\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"store\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "player", "columns": ["match_id", "outcome_name"]}], "writes": [{"table": "store", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO nz_tourism (long, faculty) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "nz_tourism", "columns": ["long", "faculty"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mountain SELECT claim_status_name, container_id, sellingprice FROM drug_approval WHERE claim_status_name > 72\"\n", "labels": {"reads": [{"table": "drug_approval", "columns": ["claim_status_name", "container_id", "sellingprice"]}], "writes": [{"table": "mountain", "columns": ["claim_status_name", "container_id", "sellingprice"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO home_game SELECT injury_date, quantity_sold, dorm_name, name_full FROM divisions WHERE injury_date > 453\"\n", "labels": {"reads": [{"table": "divisions", "columns": ["injury_date", "quantity_sold", "dorm_name", "name_full"]}], "writes": [{"table": "home_game", "columns": ["injury_date", "quantity_sold", "dorm_name", "name_full"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO tourist_attractions SELECT * FROM legacy\ncur.execute(\"SELECT num_investments, agency_id FROM platform_production LIMIT 296\")\n", "labels": {"reads": [{"table": "platform_production", "columns": ["num_investments", "agency_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO parties SELECT state_province, base_name FROM units WHERE state_province > 432\"], check=True)\n", "labels": {"reads": [{"table": "units", "columns": ["state_province", "base_name"]}], "writes": [{"table": "parties", "columns": ["state_province", "base_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT company_name, date_of_attendance FROM tourist_attraction_features LIMIT 162;\nINSERT INTO incidents SELECT clubdesc, report FROM police_stations WHERE clubdesc > 370;\nSQL\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": ["company_name", "date_of_attendance"]}, {"table": "police_stations", "columns": ["clubdesc", "report"]}], "writes": [{"table": "incidents", "columns": ["clubdesc", "report"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO stg.coupon_use_delta SELECT a.asset_details, b.length FROM aquaculture_farms a JOIN grapes b ON a.date_claim_made = b.date_claim_made\"\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": null}, {"table": "grapes", "columns": null}], "writes": [{"table": "stg.coupon_use_delta", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"brand_info\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "brand_info", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"musicsales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"organic_cosmetics\")\n", "labels": {"reads": [{"table": "musicsales", "columns": null}], "writes": [{"table": "organic_cosmetics", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table african_tourism --columns ship_agent_id,share_in_percent --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "african_tourism", "columns": ["ship_agent_id", "share_in_percent"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dispensary_name, number_cities FROM competition\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"tech_workers_union\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "competition", "columns": ["dispensary_name", "number_cities"]}], "writes": [{"table": "tech_workers_union", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO productivity SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO organic_farms (crime_date, agency) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "organic_farms", "columns": ["crime_date", "agency"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_campaigns_daily SELECT statement_details, ethical_certifications, exhibition_name, item_sold FROM crime_stats WHERE statement_details > 230\"], check=True)\n", "labels": {"reads": [{"table": "crime_stats", "columns": ["statement_details", "ethical_certifications", "exhibition_name", "item_sold"]}], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["statement_details", "ethical_certifications", "exhibition_name", "item_sold"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacemissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"policyimpact\")\n", "labels": {"reads": [{"table": "spacemissions", "columns": null}], "writes": [{"table": "policyimpact", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"producesupplier\").toPandas()\ndf[[\"long\", \"state_province_county\"]].to_sql(\"recruiters\", engine, index=False)\n", "labels": {"reads": [{"table": "producesupplier", "columns": null}], "writes": [{"table": "recruiters", "columns": ["long", "state_province_county"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table captain --target-dir /tmp/land\n", "labels": {"reads": [{"table": "captain", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"otas\")\nsrc.write.insertInto(\"ads\", overwrite=True)\n", "labels": {"reads": [{"table": "otas", "columns": null}], "writes": [{"table": "ads", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"riskassessments\").toPandas()\ndf[[\"borough\", \"trip_city\"]].to_sql(\"labor_unions\", engine, index=False)\n", "labels": {"reads": [{"table": "riskassessments", "columns": null}], "writes": [{"table": "labor_unions", "columns": ["borough", "trip_city"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT src_apid, state FROM co2price LIMIT 108\")\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO underwater_trenches SELECT account_details, attraction_name FROM wastewatertreatment WHERE account_details > 451\")\n", "labels": {"reads": [{"table": "co2price", "columns": ["src_apid", "state"]}, {"table": "wastewatertreatment", "columns": ["account_details", "attraction_name"]}], "writes": [{"table": "underwater_trenches", "columns": ["account_details", "attraction_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO military_expenditure SELECT orderdate, acc_bal, contractorname FROM ods.inventory_df WHERE orderdate > 68\")\n", "labels": {"reads": [{"table": "ods.inventory_df", "columns": ["orderdate", "acc_bal", "contractorname"]}], "writes": [{"table": "military_expenditure", "columns": ["orderdate", "acc_bal", "contractorname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table members --target-dir /tmp/land\n", "labels": {"reads": [{"table": "members", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.funding > 263).all()\n# src table: publications\nengine.execute(\"INSERT INTO tourist_destinations SELECT * FROM publications\")\n", "labels": {"reads": [{"table": "publications", "columns": null}], "writes": [{"table": "tourist_destinations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO education_programs SELECT a.is_operational, b.county_name FROM dwd.coupon_use_daily a JOIN reservations b ON a.transact_date = b.transact_date\"\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": null}, {"table": "reservations", "columns": null}], "writes": [{"table": "education_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"gamedesigndata\")\nexport_to_warehouse(df, \"geothermal_power_plants\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gamedesigndata", "columns": null}], "writes": [{"table": "geothermal_power_plants", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO pilot SELECT policy_description, class_president_vote, crop FROM uniteddefense.equipmentsales WHERE policy_description > 350\")\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": ["policy_description", "class_president_vote", "crop"]}], "writes": [{"table": "pilot", "columns": ["policy_description", "class_president_vote", "crop"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.initiativename > 98).all()\n# src table: union_membership\nengine.execute(\"INSERT INTO premises SELECT * FROM union_membership\")\n", "labels": {"reads": [{"table": "union_membership", "columns": null}], "writes": [{"table": "premises", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods.campaigns_di\").toPandas()\ndf[[\"outcome_type\", \"practice\"]].to_sql(\"communitydevelopment\", engine, index=False)\n", "labels": {"reads": [{"table": "ods.campaigns_di", "columns": null}], "writes": [{"table": "communitydevelopment", "columns": ["outcome_type", "practice"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi.inventory_daily SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model service_budget depends on renewables.renewable_projects\ndbt build --models service_budget --vars '{\"source_table\":\"renewables.renewable_projects\"}'\n", "labels": {"reads": [{"table": "renewables.renewable_projects", "columns": null}], "writes": [{"table": "service_budget", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO peacekeeping_units SELECT store_phone, time_year FROM waste WHERE store_phone > 263\"\n", "labels": {"reads": [{"table": "waste", "columns": ["store_phone", "time_year"]}], "writes": [{"table": "peacekeeping_units", "columns": ["store_phone", "time_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table automation_tech --target-dir /tmp/land\n", "labels": {"reads": [{"table": "automation_tech", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.refunds\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"social_good_education\")\n", "labels": {"reads": [{"table": "ads.refunds", "columns": null}], "writes": [{"table": "social_good_education", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tourist_attraction_features (projecttype, director) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tourist_attraction_features", "columns": ["projecttype", "director"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO textile_sourcing SELECT initiative_region, time_id, rank_in_round FROM dwd.dwd_exposure_full WHERE initiative_region > 44\"\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": ["initiative_region", "time_id", "rank_in_round"]}], "writes": [{"table": "textile_sourcing", "columns": ["initiative_region", "time_id", "rank_in_round"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO safety_incidents (arrival_date, log_entry_description) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "safety_incidents", "columns": ["arrival_date", "log_entry_description"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.stg_events_hourly SELECT membership_type, clubdesc, goldquantity, donationid FROM platformh WHERE membership_type > 491;\nEOF\n", "labels": {"reads": [{"table": "platformh", "columns": ["membership_type", "clubdesc", "goldquantity", "donationid"]}], "writes": [{"table": "stg.stg_events_hourly", "columns": ["membership_type", "clubdesc", "goldquantity", "donationid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT pieces, contributor FROM soil_moisture LIMIT 60\")\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO parts SELECT batting_average, container_count, productname, component_name FROM biotech_startups WHERE batting_average > 413\")\n", "labels": {"reads": [{"table": "soil_moisture", "columns": ["pieces", "contributor"]}, {"table": "biotech_startups", "columns": ["batting_average", "container_count", "productname", "component_name"]}], "writes": [{"table": "parts", "columns": ["batting_average", "container_count", "productname", "component_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nsql = \"INSERT INTO airportdata SELECT a.payment_method, b.tour_name FROM canals a JOIN urbanagricrop b ON a.ngo_name = b.ngo_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "canals", "columns": null}, {"table": "urbanagricrop", "columns": null}], "writes": [{"table": "airportdata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO weights (funding_round_id, financing_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "weights", "columns": ["funding_round_id", "financing_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO companies SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recyclingrates\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "recyclingrates", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO tree_species SELECT capital, driller FROM donationprograms WHERE capital > 281\"\n", "labels": {"reads": [{"table": "donationprograms", "columns": ["capital", "driller"]}], "writes": [{"table": "tree_species", "columns": ["capital", "driller"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO auto_shows SELECT strategy_id, social_impact_score, workout_duration, water_temp FROM circularsupplychain WHERE strategy_id > 240\")\n", "labels": {"reads": [{"table": "circularsupplychain", "columns": ["strategy_id", "social_impact_score", "workout_duration", "water_temp"]}], "writes": [{"table": "auto_shows", "columns": ["strategy_id", "social_impact_score", "workout_duration", "water_temp"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO africa_projects SELECT a.hiv, b.course_id FROM has_amenity a JOIN safetytests b ON a.transaction_product = b.transaction_product\"\n", "labels": {"reads": [{"table": "has_amenity", "columns": null}, {"table": "safetytests", "columns": null}], "writes": [{"table": "africa_projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"ai_for_social_good\")\npersist_to_target(df, \"manufacturing_processes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ai_for_social_good", "columns": null}], "writes": [{"table": "manufacturing_processes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM teams\", conn)\ndf.to_sql(\"culturalcompetencytrainings\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "teams", "columns": null}], "writes": [{"table": "culturalcompetencytrainings", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table space_telescopes --target-dir /tmp/land\n", "labels": {"reads": [{"table": "space_telescopes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table restaurant --target-dir /tmp/land\n", "labels": {"reads": [{"table": "restaurant", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO temperature SELECT donorgender, amount_paid, reported_date FROM ads_users_hourly WHERE donorgender > 100\"], check=True)\n", "labels": {"reads": [{"table": "ads_users_hourly", "columns": ["donorgender", "amount_paid", "reported_date"]}], "writes": [{"table": "temperature", "columns": ["donorgender", "amount_paid", "reported_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT led_by, publish_date FROM innovation_projects LIMIT 199\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO bi_orders_daily SELECT speed, g_name FROM otas WHERE speed > 354\")\n", "labels": {"reads": [{"table": "innovation_projects", "columns": ["led_by", "publish_date"]}, {"table": "otas", "columns": ["speed", "g_name"]}], "writes": [{"table": "bi_orders_daily", "columns": ["speed", "g_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO climatedata SELECT * FROM legacy\ncur.execute(\"SELECT region, checkout FROM mammals LIMIT 390\")\n", "labels": {"reads": [{"table": "mammals", "columns": ["region", "checkout"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO student_lifelong_learning SELECT claimid, roomid FROM total_capacity WHERE claimid > 30\"\n", "labels": {"reads": [{"table": "total_capacity", "columns": ["claimid", "roomid"]}], "writes": [{"table": "student_lifelong_learning", "columns": ["claimid", "roomid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_refunds_full\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"electoral_register\")\n", "labels": {"reads": [{"table": "ads_refunds_full", "columns": null}], "writes": [{"table": "electoral_register", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT is_male, attendance_date FROM mart.clicks_delta\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"acceptance\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.clicks_delta", "columns": ["is_male", "attendance_date"]}], "writes": [{"table": "acceptance", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"menu\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"militarypersonnel\")\n", "labels": {"reads": [{"table": "menu", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO voyages SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"document_types\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "document_types", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO city_properties SELECT streamid, assessment_date FROM school_bus WHERE streamid > 5\")\n", "labels": {"reads": [{"table": "school_bus", "columns": ["streamid", "assessment_date"]}], "writes": [{"table": "city_properties", "columns": ["streamid", "assessment_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO haircaresales SELECT is_eco_friendly, container_id, launch_company FROM broadband_plans WHERE is_eco_friendly > 107\"\n", "labels": {"reads": [{"table": "broadband_plans", "columns": ["is_eco_friendly", "container_id", "launch_company"]}], "writes": [{"table": "haircaresales", "columns": ["is_eco_friendly", "container_id", "launch_company"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"nutrition_facts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_shipping.cargo\")\n", "labels": {"reads": [{"table": "nutrition_facts", "columns": null}], "writes": [{"table": "ocean_shipping.cargo", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM block\"\n", "labels": {"reads": [{"table": "block", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO exhibition_visitors SELECT * FROM legacy\ncur.execute(\"SELECT exit_date, museum_id FROM territory.human_rights_data LIMIT 217\")\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": ["exit_date", "museum_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO academic_publications SELECT document_structure_description, domestic_passengers, precedent_id, role_code FROM categories WHERE document_structure_description > 161\"\n", "labels": {"reads": [{"table": "categories", "columns": ["document_structure_description", "domestic_passengers", "precedent_id", "role_code"]}], "writes": [{"table": "academic_publications", "columns": ["document_structure_description", "domestic_passengers", "precedent_id", "role_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT visit_date, apt_number FROM dws.exposure LIMIT 212\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO research_grants SELECT personnelbranch, policy_type_code, incidents, lot_details FROM public.trips_by_day_train WHERE personnelbranch > 115\")\n", "labels": {"reads": [{"table": "dws.exposure", "columns": ["visit_date", "apt_number"]}, {"table": "public.trips_by_day_train", "columns": ["personnelbranch", "policy_type_code", "incidents", "lot_details"]}], "writes": [{"table": "research_grants", "columns": ["personnelbranch", "policy_type_code", "incidents", "lot_details"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cuisine (claim_number, trip_duration) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cuisine", "columns": ["claim_number", "trip_duration"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT subject_area_id, brand_id FROM party_host LIMIT 197\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "party_host", "columns": ["subject_area_id", "brand_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table building --columns event_name,location_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "building", "columns": ["event_name", "location_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_inventory_hourly SELECT low_estimate, train_type, mealname FROM journal WHERE low_estimate > 14\"\n", "labels": {"reads": [{"table": "journal", "columns": ["low_estimate", "train_type", "mealname"]}], "writes": [{"table": "stg.stg_inventory_hourly", "columns": ["low_estimate", "train_type", "mealname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO authenticationlogs SELECT 1\"\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"department_publications\").toPandas()\ndf[[\"complaint_id\", \"dates_active\"]].to_sql(\"bi.bi_events_df\", engine, index=False)\n", "labels": {"reads": [{"table": "department_publications", "columns": null}], "writes": [{"table": "bi.bi_events_df", "columns": ["complaint_id", "dates_active"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO government.region SELECT nutrient_level, petid, transact_date, installed_date FROM emergency_responses WHERE nutrient_level > 16\"\n", "labels": {"reads": [{"table": "emergency_responses", "columns": ["nutrient_level", "petid", "transact_date", "installed_date"]}], "writes": [{"table": "government.region", "columns": ["nutrient_level", "petid", "transact_date", "installed_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ocean_temperatures SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT claim_date, damage_millions_usd FROM furniture LIMIT 468\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ingredient_sourcing SELECT cell_mobile_phone_number, pd_id, tripdatetime, cropid FROM aircraft_flights WHERE cell_mobile_phone_number > 466\")\n", "labels": {"reads": [{"table": "furniture", "columns": ["claim_date", "damage_millions_usd"]}, {"table": "aircraft_flights", "columns": ["cell_mobile_phone_number", "pd_id", "tripdatetime", "cropid"]}], "writes": [{"table": "ingredient_sourcing", "columns": ["cell_mobile_phone_number", "pd_id", "tripdatetime", "cropid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.inventory_df --columns union_name,song_release_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.inventory_df", "columns": ["union_name", "song_release_year"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nsql = \"INSERT INTO storage_tech SELECT a.low_temperature, b.volunteerhourid FROM design_standards a JOIN nba b ON a.chip_model = b.chip_model\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "design_standards", "columns": null}, {"table": "nba", "columns": null}], "writes": [{"table": "storage_tech", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model product_catalog depends on mappinglengths\ndbt build --models product_catalog --vars 'source: mappinglengths'\n", "labels": {"reads": [{"table": "mappinglengths", "columns": null}], "writes": [{"table": "product_catalog", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO flight_safety SELECT a.energy_consumption, b.omim FROM vulnerabilities a JOIN ref_document_status b ON a.end_speed = b.end_speed\"\n", "labels": {"reads": [{"table": "vulnerabilities", "columns": null}, {"table": "ref_document_status", "columns": null}], "writes": [{"table": "flight_safety", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"research.species\")\nupsert_to_store(df, \"constructors\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "research.species", "columns": null}], "writes": [{"table": "constructors", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM warehouses\", conn)\ndf.to_sql(\"military_equipment_maintenance\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "warehouses", "columns": null}], "writes": [{"table": "military_equipment_maintenance", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"platformh\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"evidence_based_policies\")\n", "labels": {"reads": [{"table": "platformh", "columns": null}], "writes": [{"table": "evidence_based_policies", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO tencel_sources SELECT bias_score, customer FROM landfill_capacity WHERE bias_score > 498;\nEOF\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": ["bias_score", "customer"]}], "writes": [{"table": "tencel_sources", "columns": ["bias_score", "customer"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tech_accessibility_funding (document_description, park_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tech_accessibility_funding", "columns": ["document_description", "park_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT songid, spacecraft_id FROM dws.dws_orders LIMIT 5;\nINSERT INTO investor_activities SELECT host_id, has_spf, energy_generated, enzyme_id FROM freshwaterfinfish WHERE host_id > 441;\nSQL\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": ["songid", "spacecraft_id"]}, {"table": "freshwaterfinfish", "columns": ["host_id", "has_spf", "energy_generated", "enzyme_id"]}], "writes": [{"table": "investor_activities", "columns": ["host_id", "has_spf", "energy_generated", "enzyme_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO field SELECT salesperson_id, materialtype FROM party_forms WHERE salesperson_id > 237;\nEOF\n", "labels": {"reads": [{"table": "party_forms", "columns": ["salesperson_id", "materialtype"]}], "writes": [{"table": "field", "columns": ["salesperson_id", "materialtype"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"portfolios\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"casebilling\")\n", "labels": {"reads": [{"table": "portfolios", "columns": null}], "writes": [{"table": "casebilling", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT stateid, order_quantity FROM stores LIMIT 219\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO ods.clicks_delta SELECT activity_date, customer_address, incident_type, material_id FROM swimmer WHERE activity_date > 459\")\n", "labels": {"reads": [{"table": "stores", "columns": ["stateid", "order_quantity"]}, {"table": "swimmer", "columns": ["activity_date", "customer_address", "incident_type", "material_id"]}], "writes": [{"table": "ods.clicks_delta", "columns": ["activity_date", "customer_address", "incident_type", "material_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT investment, individual_middle_name FROM industrial_customers LIMIT 268;\nINSERT INTO education SELECT cid, labordate FROM monthly_temp WHERE cid > 440;\nSQL\n", "labels": {"reads": [{"table": "industrial_customers", "columns": ["investment", "individual_middle_name"]}, {"table": "monthly_temp", "columns": ["cid", "labordate"]}], "writes": [{"table": "education", "columns": ["cid", "labordate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"stg_users_daily\")\nwrite_to_output(df, \"jupiter_missions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "stg_users_daily", "columns": null}], "writes": [{"table": "jupiter_missions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO management SELECT vendorid, manager_id FROM researchgrants WHERE vendorid > 440\"\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["vendorid", "manager_id"]}], "writes": [{"table": "management", "columns": ["vendorid", "manager_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO veteran_occupations SELECT * FROM legacy\ncur.execute(\"SELECT aid, product_category_code FROM teams LIMIT 216\")\n", "labels": {"reads": [{"table": "teams", "columns": ["aid", "product_category_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table attendee_demographics --target-dir /tmp/land\n", "labels": {"reads": [{"table": "attendee_demographics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO yoga SELECT countryid, attorney, trader_id, capacity_mw FROM ods.ods_exposure_delta WHERE countryid > 67\")\n", "labels": {"reads": [{"table": "ods.ods_exposure_delta", "columns": ["countryid", "attorney", "trader_id", "capacity_mw"]}], "writes": [{"table": "yoga", "columns": ["countryid", "attorney", "trader_id", "capacity_mw"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT amount_due, garment FROM dw.member_point_daily\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"recruiters\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dw.member_point_daily", "columns": ["amount_due", "garment"]}], "writes": [{"table": "recruiters", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cerium_production\").toPandas()\ndf[[\"lieutenant_governor\", \"city_traffic_speed\"]].to_sql(\"train_station\", engine, index=False)\n", "labels": {"reads": [{"table": "cerium_production", "columns": null}], "writes": [{"table": "train_station", "columns": ["lieutenant_governor", "city_traffic_speed"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM training_programs\", conn)\ndf.to_sql(\"satisfaction\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "training_programs", "columns": null}], "writes": [{"table": "satisfaction", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads.ads_payments_delta SELECT pilot, typical_buying_price, exit_type, count_id FROM bookings WHERE pilot > 257;\nEOF\n", "labels": {"reads": [{"table": "bookings", "columns": ["pilot", "typical_buying_price", "exit_type", "count_id"]}], "writes": [{"table": "ads.ads_payments_delta", "columns": ["pilot", "typical_buying_price", "exit_type", "count_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO flights SELECT machine, num_attendees, next_maintenance, process_id FROM carbon_offset_programs WHERE machine > 306\"\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": ["machine", "num_attendees", "next_maintenance", "process_id"]}], "writes": [{"table": "flights", "columns": ["machine", "num_attendees", "next_maintenance", "process_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO drama_workshop_groups (payment_id, prepnurse) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "drama_workshop_groups", "columns": ["payment_id", "prepnurse"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO influencers SELECT supplier_company_id, market_id FROM manager_award WHERE supplier_company_id > 127\"], check=True)\n", "labels": {"reads": [{"table": "manager_award", "columns": ["supplier_company_id", "market_id"]}], "writes": [{"table": "influencers", "columns": ["supplier_company_id", "market_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model therapy_attendance depends on stg.stg_risk_score_df\ndbt build --select therapy_attendance --vars 'source: stg.stg_risk_score_df'\n", "labels": {"reads": [{"table": "stg.stg_risk_score_df", "columns": null}], "writes": [{"table": "therapy_attendance", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO therapists SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bank_info SELECT accreditation_type, enable_location_tracking FROM climateresearch WHERE accreditation_type > 276\"\n", "labels": {"reads": [{"table": "climateresearch", "columns": ["accreditation_type", "enable_location_tracking"]}], "writes": [{"table": "bank_info", "columns": ["accreditation_type", "enable_location_tracking"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM waste\", conn)\ndf.to_sql(\"community_health_centers\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "waste", "columns": null}], "writes": [{"table": "community_health_centers", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT lat, decision FROM vocals LIMIT 408;\nINSERT INTO researchprojects SELECT used_kb, claim_status_name, date FROM satellite_missions_large WHERE used_kb > 193;\nSQL\n", "labels": {"reads": [{"table": "vocals", "columns": ["lat", "decision"]}, {"table": "satellite_missions_large", "columns": ["used_kb", "claim_status_name", "date"]}], "writes": [{"table": "researchprojects", "columns": ["used_kb", "claim_status_name", "date"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO industry_funding (framework_id, claim_status_description) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "industry_funding", "columns": ["framework_id", "claim_status_description"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table dwd.dwd_member_point_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.dwd_member_point_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO city.community_policing SELECT bike_id, missions, evaluationid, completed FROM shipments WHERE bike_id > 224\")\n", "labels": {"reads": [{"table": "shipments", "columns": ["bike_id", "missions", "evaluationid", "completed"]}], "writes": [{"table": "city.community_policing", "columns": ["bike_id", "missions", "evaluationid", "completed"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO appointments SELECT days, date_and_date FROM rnd_budget WHERE days > 325\")\n", "labels": {"reads": [{"table": "rnd_budget", "columns": ["days", "date_and_date"]}], "writes": [{"table": "appointments", "columns": ["days", "date_and_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO organic_products SELECT days_held, stu_gpa FROM unions WHERE days_held > 352\")\n", "labels": {"reads": [{"table": "unions", "columns": ["days_held", "stu_gpa"]}], "writes": [{"table": "organic_products", "columns": ["days_held", "stu_gpa"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO phone_market SELECT a.case_type, b.violation_id FROM investor_activities a JOIN vessel_capacity b ON a.stat_id = b.stat_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}, {"table": "vessel_capacity", "columns": null}], "writes": [{"table": "phone_market", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO vessel_incident_count SELECT amount_due, height, contract_id, policy_count FROM storage_tech WHERE amount_due > 355\")\n", "labels": {"reads": [{"table": "storage_tech", "columns": ["amount_due", "height", "contract_id", "policy_count"]}], "writes": [{"table": "vessel_incident_count", "columns": ["amount_due", "height", "contract_id", "policy_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table research_staff --columns authorder,asset_model --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "research_staff", "columns": ["authorder", "asset_model"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table movie --columns environmental_impact_score,cruelty_free --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "movie", "columns": ["environmental_impact_score", "cruelty_free"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"state_water_usage\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "state_water_usage", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"danceevents\").toPandas()\ndf[[\"noise_level\", \"minutes\"]].to_sql(\"endowment\", engine, index=False)\n", "labels": {"reads": [{"table": "danceevents", "columns": null}], "writes": [{"table": "endowment", "columns": ["noise_level", "minutes"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO demographics SELECT date_of_birth, total_value_purchased FROM wells WHERE date_of_birth > 46\"\n", "labels": {"reads": [{"table": "wells", "columns": ["date_of_birth", "total_value_purchased"]}], "writes": [{"table": "demographics", "columns": ["date_of_birth", "total_value_purchased"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO vessel_types SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO training_programs SELECT games, schedule_date, ei_category FROM bike_stations WHERE games > 425\")\n", "labels": {"reads": [{"table": "bike_stations", "columns": ["games", "schedule_date", "ei_category"]}], "writes": [{"table": "training_programs", "columns": ["games", "schedule_date", "ei_category"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.stg_events_hourly SELECT artist, attack_id, union_members, trial_status FROM membership_data WHERE artist > 312;\nEOF\n", "labels": {"reads": [{"table": "membership_data", "columns": ["artist", "attack_id", "union_members", "trial_status"]}], "writes": [{"table": "stg.stg_events_hourly", "columns": ["artist", "attack_id", "union_members", "trial_status"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO imagery_archive SELECT amount_waste, settlement_amount, editor_id, label FROM food_justice_orgs WHERE amount_waste > 289\"\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": ["amount_waste", "settlement_amount", "editor_id", "label"]}], "writes": [{"table": "imagery_archive", "columns": ["amount_waste", "settlement_amount", "editor_id", "label"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shipping_agent_code, building_manager FROM issues LIMIT 420\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "issues", "columns": ["shipping_agent_code", "building_manager"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table satellites --columns musical_id,primary_conference --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "satellites", "columns": ["musical_id", "primary_conference"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT subscription_start_date, circular_supply_chain FROM mart.risk_score_df LIMIT 205\")\nimport logging\nspark.sql(\"INSERT INTO sales_quarterly SELECT beds, date_of_notes, height FROM reporters WHERE beds > 119\")\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["subscription_start_date", "circular_supply_chain"]}, {"table": "reporters", "columns": ["beds", "date_of_notes", "height"]}], "writes": [{"table": "sales_quarterly", "columns": ["beds", "date_of_notes", "height"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO timed_status_of_things (customer_status_code, asset_details) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "timed_status_of_things", "columns": ["customer_status_code", "asset_details"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tourism_centers SELECT 1\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO green_certification SELECT profits_in_billion, phone_id, volunteerhourid, fault_status FROM movies WHERE profits_in_billion > 478\"\n", "labels": {"reads": [{"table": "movies", "columns": ["profits_in_billion", "phone_id", "volunteerhourid", "fault_status"]}], "writes": [{"table": "green_certification", "columns": ["profits_in_billion", "phone_id", "volunteerhourid", "fault_status"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fans SELECT pricepergram, salary FROM veterans WHERE pricepergram > 399\"\n", "labels": {"reads": [{"table": "veterans", "columns": ["pricepergram", "salary"]}], "writes": [{"table": "fans", "columns": ["pricepergram", "salary"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ai_for_social_good depends on satellites_by_country\ndbt run --models ai_for_social_good --vars 'source: satellites_by_country'\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}], "writes": [{"table": "ai_for_social_good", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT contractor_id, width FROM climate_projects LIMIT 495\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "climate_projects", "columns": ["contractor_id", "width"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table portfolios --columns character,is_commercial --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "portfolios", "columns": ["character", "is_commercial"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ocean_salinity SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO pollution_control_initiatives SELECT * FROM legacy\ncur.execute(\"SELECT sales_transaction_id, trips FROM volunteer_hours LIMIT 209\")\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": ["sales_transaction_id", "trips"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model clothingsales depends on dws.dws_events_df\ndbt build --select clothingsales --vars '{\"src\":\"dws.dws_events_df\"}'\n", "labels": {"reads": [{"table": "dws.dws_events_df", "columns": null}], "writes": [{"table": "clothingsales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO daily_industrial_water_usage SELECT a.indigenous, b.policyname FROM flight_emissions a JOIN drugs b ON a.excavationid = b.excavationid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "flight_emissions", "columns": null}, {"table": "drugs", "columns": null}], "writes": [{"table": "daily_industrial_water_usage", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO exit_strategies SELECT a.reported, b.value FROM aid_missions a JOIN production b ON a.founder_race = b.founder_race\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "aid_missions", "columns": null}, {"table": "production", "columns": null}], "writes": [{"table": "exit_strategies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hockey_players\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"news_reporting\")\n", "labels": {"reads": [{"table": "hockey_players", "columns": null}], "writes": [{"table": "news_reporting", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"mental_health_parity\")\nupsert_to_output(df, \"bi.bi_inventory_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mental_health_parity", "columns": null}], "writes": [{"table": "bi.bi_inventory_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.participatedinesports > 113).all()\n# src table: county\nengine.execute(\"INSERT INTO ods.shipments_df SELECT * FROM county\")\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "ods.shipments_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO animal_populations SELECT volume_id, mental_health_resource_access, communityname, safety_id FROM al_jazeera_data WHERE volume_id > 157\"\n", "labels": {"reads": [{"table": "al_jazeera_data", "columns": ["volume_id", "mental_health_resource_access", "communityname", "safety_id"]}], "writes": [{"table": "animal_populations", "columns": ["volume_id", "mental_health_resource_access", "communityname", "safety_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO ads.ads_exposure_di SELECT siteid, green_building_id FROM mission WHERE siteid > 126\"\n", "labels": {"reads": [{"table": "mission", "columns": ["siteid", "green_building_id"]}], "writes": [{"table": "ads.ads_exposure_di", "columns": ["siteid", "green_building_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO field SELECT recycler_id, asset_model, goal_id, stream_id FROM mentalhealthprovider WHERE recycler_id > 441\")\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": ["recycler_id", "asset_model", "goal_id", "stream_id"]}], "writes": [{"table": "field", "columns": ["recycler_id", "asset_model", "goal_id", "stream_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO musicgenre SELECT doctor_id, total_distance, date_left_staff, destinationid FROM criminalcases WHERE doctor_id > 473\")\n", "labels": {"reads": [{"table": "criminalcases", "columns": ["doctor_id", "total_distance", "date_left_staff", "destinationid"]}], "writes": [{"table": "musicgenre", "columns": ["doctor_id", "total_distance", "date_left_staff", "destinationid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"dw.dw_orders_hourly\")\nwrite_to_warehouse(df, \"bi.clicks_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dw.dw_orders_hourly", "columns": null}], "writes": [{"table": "bi.clicks_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table ai_projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ai_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vessel_capacity SELECT era, birthdate, pricepergram FROM bi.bi_payments_df WHERE era > 444\"\n", "labels": {"reads": [{"table": "bi.bi_payments_df", "columns": ["era", "birthdate", "pricepergram"]}], "writes": [{"table": "vessel_capacity", "columns": ["era", "birthdate", "pricepergram"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vocals SELECT tourists, death_year, schedule_id FROM shrimp_farms WHERE tourists > 221\"], check=True)\n", "labels": {"reads": [{"table": "shrimp_farms", "columns": ["tourists", "death_year", "schedule_id"]}], "writes": [{"table": "vocals", "columns": ["tourists", "death_year", "schedule_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table onlineengagement --columns sales,partid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "onlineengagement", "columns": ["sales", "partid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ethicalaibudget (hospitalid, effort) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ethicalaibudget", "columns": ["hospitalid", "effort"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model habitat depends on courtcases\ndbt run -s habitat --vars '{\"source_table\":\"courtcases\"}'\n", "labels": {"reads": [{"table": "courtcases", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nsql = \"INSERT INTO classicgame SELECT a.jan, b.incident_category FROM weekly_weather a JOIN workforce_training b ON a.comments = b.comments\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "weekly_weather", "columns": null}, {"table": "workforce_training", "columns": null}], "writes": [{"table": "classicgame", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO org_donation SELECT * FROM legacy\ncur.execute(\"SELECT volume, fund_type FROM workforce_development LIMIT 215\")\n", "labels": {"reads": [{"table": "workforce_development", "columns": ["volume", "fund_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"traffic_violations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bridgeconstruction\")\n", "labels": {"reads": [{"table": "traffic_violations", "columns": null}], "writes": [{"table": "bridgeconstruction", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"dws_clicks_di\")\nupsert_to_target(df, \"coal_reserves\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": null}], "writes": [{"table": "coal_reserves", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO manager_award SELECT a.menuitemid, b.item_id FROM apartments a JOIN phone_market b ON a.vaccine_name = b.vaccine_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "apartments", "columns": null}, {"table": "phone_market", "columns": null}], "writes": [{"table": "manager_award", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artcollection SELECT working_horses, labor_hour_id FROM construction_labor_stats WHERE working_horses > 29\"\n", "labels": {"reads": [{"table": "construction_labor_stats", "columns": ["working_horses", "labor_hour_id"]}], "writes": [{"table": "artcollection", "columns": ["working_horses", "labor_hour_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table project_issues --target-dir /tmp/land\n", "labels": {"reads": [{"table": "project_issues", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO monitoring_zones SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO experts SELECT build_date, artworkyear, views, person_id FROM genetics.experiments WHERE build_date > 399\")\n", "labels": {"reads": [{"table": "genetics.experiments", "columns": ["build_date", "artworkyear", "views", "person_id"]}], "writes": [{"table": "experts", "columns": ["build_date", "artworkyear", "views", "person_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT num_shariah_compliant_investments, excavation_site FROM player_demographics LIMIT 349\")\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO circularsupplychain SELECT founder_lgbtq, vrgameid, mappingid FROM council_tax WHERE founder_lgbtq > 22\")\n", "labels": {"reads": [{"table": "player_demographics", "columns": ["num_shariah_compliant_investments", "excavation_site"]}, {"table": "council_tax", "columns": ["founder_lgbtq", "vrgameid", "mappingid"]}], "writes": [{"table": "circularsupplychain", "columns": ["founder_lgbtq", "vrgameid", "mappingid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT faculty_id, campaign_id FROM game_sessions LIMIT 402\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "game_sessions", "columns": ["faculty_id", "campaign_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_cart_item\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"operation\")\n", "labels": {"reads": [{"table": "dws_cart_item", "columns": null}], "writes": [{"table": "operation", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO researchgrants SELECT trial_id, hours_served, zone_id FROM marine_life_sightings WHERE trial_id > 488\"\n", "labels": {"reads": [{"table": "marine_life_sightings", "columns": ["trial_id", "hours_served", "zone_id"]}], "writes": [{"table": "researchgrants", "columns": ["trial_id", "hours_served", "zone_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_sessions_hourly SELECT a.dorm_name, b.ssn FROM songs a JOIN expenditure b ON a.length_meters = b.length_meters\"\n", "labels": {"reads": [{"table": "songs", "columns": null}, {"table": "expenditure", "columns": null}], "writes": [{"table": "bi.bi_sessions_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO attorneylocationyear SELECT a.contract_value, b.artworkid FROM staff_members a JOIN automation_tech b ON a.county_name = b.county_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "staff_members", "columns": null}, {"table": "automation_tech", "columns": null}], "writes": [{"table": "attorneylocationyear", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO manufacturersustainability SELECT * FROM legacy\ncur.execute(\"SELECT location_id, lawyer_name FROM public_participation LIMIT 60\")\n", "labels": {"reads": [{"table": "public_participation", "columns": ["location_id", "lawyer_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO gold SELECT ticket_price, creator, production_date, component_type FROM electric_taxis WHERE ticket_price > 21\"\n", "labels": {"reads": [{"table": "electric_taxis", "columns": ["ticket_price", "creator", "production_date", "component_type"]}], "writes": [{"table": "gold", "columns": ["ticket_price", "creator", "production_date", "component_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO orders SELECT country_of_origin, producerid FROM benefits_overpayments WHERE country_of_origin > 106\"\n", "labels": {"reads": [{"table": "benefits_overpayments", "columns": ["country_of_origin", "producerid"]}], "writes": [{"table": "orders", "columns": ["country_of_origin", "producerid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT valuation, mental_health_score FROM contract_transactions LIMIT 113;\nINSERT INTO tech_volunteers SELECT artist, grade, years_working FROM emerging_markets.digital_assets WHERE artist > 404;\nSQL\n", "labels": {"reads": [{"table": "contract_transactions", "columns": ["valuation", "mental_health_score"]}, {"table": "emerging_markets.digital_assets", "columns": ["artist", "grade", "years_working"]}], "writes": [{"table": "tech_volunteers", "columns": ["artist", "grade", "years_working"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table constructionlaborstatistics --target-dir /tmp/land\n", "labels": {"reads": [{"table": "constructionlaborstatistics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO patient_satisfaction (transaction_product, energy_generated) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "patient_satisfaction", "columns": ["transaction_product", "energy_generated"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climber (subscriber_type, years_operating) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climber", "columns": ["subscriber_type", "years_operating"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"disasters\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"bi.bi_shipments\")\n", "labels": {"reads": [{"table": "disasters", "columns": null}], "writes": [{"table": "bi.bi_shipments", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO bi.member_point_full SELECT launch_id, workername, years_operating FROM seafood WHERE launch_id > 149\"\n", "labels": {"reads": [{"table": "seafood", "columns": ["launch_id", "workername", "years_operating"]}], "writes": [{"table": "bi.member_point_full", "columns": ["launch_id", "workername", "years_operating"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO safety_incidents_india SELECT productionrate, total_distance, registration_id, injury FROM conservation WHERE productionrate > 444\"\n", "labels": {"reads": [{"table": "conservation", "columns": ["productionrate", "total_distance", "registration_id", "injury"]}], "writes": [{"table": "safety_incidents_india", "columns": ["productionrate", "total_distance", "registration_id", "injury"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO department_stores SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model satellites depends on mart.mart_products_hourly\ndbt run -s satellites --vars '{\"src\":\"mart.mart_products_hourly\"}'\n", "labels": {"reads": [{"table": "mart.mart_products_hourly", "columns": null}], "writes": [{"table": "satellites", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO coffee_prices SELECT * FROM legacy\ncur.execute(\"SELECT date_of_attendance, premise_details FROM clinics_sa LIMIT 222\")\n", "labels": {"reads": [{"table": "clinics_sa", "columns": ["date_of_attendance", "premise_details"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model wrestler depends on dysprosiumproduction\ndbt run -s wrestler --vars '{\"source_table\":\"dysprosiumproduction\"}'\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}], "writes": [{"table": "wrestler", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO investors (sustainability_score, brand_mentioned) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "investors", "columns": ["sustainability_score", "brand_mentioned"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_refunds_hourly SELECT a.site, b.reo_type FROM climate_adaptation_projects a JOIN mining_operation b ON a.votes = b.votes\"\n", "labels": {"reads": [{"table": "climate_adaptation_projects", "columns": null}, {"table": "mining_operation", "columns": null}], "writes": [{"table": "ads.ads_refunds_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO tech_for_social_good SELECT supply_volume, energytype FROM model_data WHERE supply_volume > 408\"\n", "labels": {"reads": [{"table": "model_data", "columns": ["supply_volume", "energytype"]}], "writes": [{"table": "tech_for_social_good", "columns": ["supply_volume", "energytype"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"guests\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "guests", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.bi_member_point\")\nsrc.write.insertInto(\"waste_types\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.bi_member_point", "columns": null}], "writes": [{"table": "waste_types", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO platform (material_date, farm_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "platform", "columns": ["material_date", "farm_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainable_tourism_practices SELECT circularsupplychain, support_rep_id, program_name, violation_count FROM authenticationlogs WHERE circularsupplychain > 66\"\n", "labels": {"reads": [{"table": "authenticationlogs", "columns": ["circularsupplychain", "support_rep_id", "program_name", "violation_count"]}], "writes": [{"table": "sustainable_tourism_practices", "columns": ["circularsupplychain", "support_rep_id", "program_name", "violation_count"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_exposure_hourly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ods.ods_sessions_df\")\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": null}], "writes": [{"table": "ods.ods_sessions_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO resource_extraction SELECT participant, transaction_type_code, date_formed, section_title FROM forest_species WHERE participant > 390\"], check=True)\n", "labels": {"reads": [{"table": "forest_species", "columns": ["participant", "transaction_type_code", "date_formed", "section_title"]}], "writes": [{"table": "resource_extraction", "columns": ["participant", "transaction_type_code", "date_formed", "section_title"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO permit SELECT fine, membership FROM autonomous_testing WHERE fine > 435\"\n", "labels": {"reads": [{"table": "autonomous_testing", "columns": ["fine", "membership"]}], "writes": [{"table": "permit", "columns": ["fine", "membership"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg_payments_hourly SELECT meter_300, emp_fname, practiceid FROM e_scooter_trips WHERE meter_300 > 359\"], check=True)\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": ["meter_300", "emp_fname", "practiceid"]}], "writes": [{"table": "stg_payments_hourly", "columns": ["meter_300", "emp_fname", "practiceid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vessel_registry\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"workout_sessions\")\n", "labels": {"reads": [{"table": "vessel_registry", "columns": null}], "writes": [{"table": "workout_sessions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM microfinance_clients\"\n", "labels": {"reads": [{"table": "microfinance_clients", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"regulatoryframeworksbycountry\")\npush_to_output(df, \"invoice_lines\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "regulatoryframeworksbycountry", "columns": null}], "writes": [{"table": "invoice_lines", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO community_development.transactions SELECT * FROM legacy\ncur.execute(\"SELECT countryname, visitor_id FROM intelligence_personnel LIMIT 235\")\n", "labels": {"reads": [{"table": "intelligence_personnel", "columns": ["countryname", "visitor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model yoga depends on regulatory_compliance\ndbt build --models yoga --vars '{\"source_table\":\"regulatory_compliance\"}'\n", "labels": {"reads": [{"table": "regulatory_compliance", "columns": null}], "writes": [{"table": "yoga", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.trial_id > 355).all()\n# src table: passengers\nengine.execute(\"INSERT INTO performance_scores SELECT * FROM passengers\")\n", "labels": {"reads": [{"table": "passengers", "columns": null}], "writes": [{"table": "performance_scores", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"electric_vehicles\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "electric_vehicles", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_table(ctx, \"takes\")\nsave_to_warehouse(df, \"platform\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "takes", "columns": null}], "writes": [{"table": "platform", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM building_stats\", conn)\ndf.to_sql(\"solar_farms\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "building_stats", "columns": null}], "writes": [{"table": "solar_farms", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO areas SELECT archaeologist_id, waste_amount, grant_end_date, membership_amount FROM art WHERE archaeologist_id > 140\"], check=True)\n", "labels": {"reads": [{"table": "art", "columns": ["archaeologist_id", "waste_amount", "grant_end_date", "membership_amount"]}], "writes": [{"table": "areas", "columns": ["archaeologist_id", "waste_amount", "grant_end_date", "membership_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT budgeted, organic FROM gamedata\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"auto_show\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "gamedata", "columns": ["budgeted", "organic"]}], "writes": [{"table": "auto_show", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT county_id, gross_worldwide FROM ethicalaibudget LIMIT 351\")\nif not rows:\n    logger.warning('empty result')\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO genderdistribution SELECT hoursspent, productid, excavationid FROM mental_health_center WHERE hoursspent > 499\")\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": ["county_id", "gross_worldwide"]}, {"table": "mental_health_center", "columns": ["hoursspent", "productid", "excavationid"]}], "writes": [{"table": "genderdistribution", "columns": ["hoursspent", "productid", "excavationid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM na_schema.hospitals\", conn)\ndf.to_sql(\"sales_by_quarter\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "na_schema.hospitals", "columns": null}], "writes": [{"table": "sales_by_quarter", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO lenders SELECT a.ship_date, b.menu_id FROM healthcare_access_v2 a JOIN drama_workshop_groups b ON a.athlete_id = b.athlete_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": null}, {"table": "drama_workshop_groups", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO department_store_chain SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"match_result\")\nsrc.write.insertInto(\"storage_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": [{"table": "storage_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"temperaturerecords\")\nsrc.write.insertInto(\"vessel_tracking\", overwrite=True)\n", "labels": {"reads": [{"table": "temperaturerecords", "columns": null}], "writes": [{"table": "vessel_tracking", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dispensary_sales (sales_details, participant_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dispensary_sales", "columns": ["sales_details", "participant_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM economic_diversification_efforts\", conn)\ndf.to_sql(\"asset_parts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": null}], "writes": [{"table": "asset_parts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO haircaresales SELECT artifact_id, course_id, employer_organisation_id FROM bi.device_log_hourly WHERE artifact_id > 44;\nEOF\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": ["artifact_id", "course_id", "employer_organisation_id"]}], "writes": [{"table": "haircaresales", "columns": ["artifact_id", "course_id", "employer_organisation_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model playerscores depends on deliveryaddresses\ndbt build --select playerscores --vars '{\"src\":\"deliveryaddresses\"}'\n", "labels": {"reads": [{"table": "deliveryaddresses", "columns": null}], "writes": [{"table": "playerscores", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incident_region, element FROM union_membership LIMIT 433\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO maintenance_requests SELECT festival_id, artworkid, staff_address_id, ad_id FROM developers WHERE festival_id > 69\")\n", "labels": {"reads": [{"table": "union_membership", "columns": ["incident_region", "element"]}, {"table": "developers", "columns": ["festival_id", "artworkid", "staff_address_id", "ad_id"]}], "writes": [{"table": "maintenance_requests", "columns": ["festival_id", "artworkid", "staff_address_id", "ad_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.content > 257).all()\n# src table: boston_emergency_response\nengine.execute(\"INSERT INTO stg.sessions_full SELECT * FROM boston_emergency_response\")\n", "labels": {"reads": [{"table": "boston_emergency_response", "columns": null}], "writes": [{"table": "stg.sessions_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO volunteer_events (attendance_id, wage_increase) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "volunteer_events", "columns": ["attendance_id", "wage_increase"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.party_email > 237).all()\n# src table: dwd.inventory_df\nengine.execute(\"INSERT INTO factory_water SELECT * FROM dwd.inventory_df\")\n", "labels": {"reads": [{"table": "dwd.inventory_df", "columns": null}], "writes": [{"table": "factory_water", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO hospitals SELECT a.investment_id, b.clubdesc FROM companies_extended a JOIN malicious_activity b ON a.cultivatorname = b.cultivatorname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "companies_extended", "columns": null}, {"table": "malicious_activity", "columns": null}], "writes": [{"table": "hospitals", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO carbon_prices SELECT a.access_date, b.case_status FROM product_sales a JOIN testtypes b ON a.wheels = b.wheels\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "product_sales", "columns": null}, {"table": "testtypes", "columns": null}], "writes": [{"table": "carbon_prices", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO staff_department_assignments SELECT num_investments, do_value, inspectionid FROM apac_hotel_views WHERE num_investments > 387\"\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["num_investments", "do_value", "inspectionid"]}], "writes": [{"table": "staff_department_assignments", "columns": ["num_investments", "do_value", "inspectionid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO military_sales SELECT amount, crispr_id, eventattendance FROM project_duration WHERE amount > 226\"], check=True)\n", "labels": {"reads": [{"table": "project_duration", "columns": ["amount", "crispr_id", "eventattendance"]}], "writes": [{"table": "military_sales", "columns": ["amount", "crispr_id", "eventattendance"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.products_di\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"spaceradar\")\n", "labels": {"reads": [{"table": "dwd.products_di", "columns": null}], "writes": [{"table": "spaceradar", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"factory_connections\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"aquaticfarm\")\n", "labels": {"reads": [{"table": "factory_connections", "columns": null}], "writes": [{"table": "aquaticfarm", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO maintenance SELECT num_investments, facilityid, artwork_id FROM trainings WHERE num_investments > 5\")\n", "labels": {"reads": [{"table": "trainings", "columns": ["num_investments", "facilityid", "artwork_id"]}], "writes": [{"table": "maintenance", "columns": ["num_investments", "facilityid", "artwork_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"part_faults\").toPandas()\ndf[[\"instrument\", \"explainability_score\"]].to_sql(\"customer_transactions\", engine, index=False)\n", "labels": {"reads": [{"table": "part_faults", "columns": null}], "writes": [{"table": "customer_transactions", "columns": ["instrument", "explainability_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table military_personnel_africa --columns reader_id,blockfloor --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "military_personnel_africa", "columns": ["reader_id", "blockfloor"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO recyclingcenters SELECT regional_population, points FROM dp_articles WHERE regional_population > 425\"\n", "labels": {"reads": [{"table": "dp_articles", "columns": ["regional_population", "points"]}], "writes": [{"table": "recyclingcenters", "columns": ["regional_population", "points"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table overwatch_scores --columns domestic_passengers,police_officers --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "overwatch_scores", "columns": ["domestic_passengers", "police_officers"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sensor_reading, underrepresented_community FROM solana_transactions LIMIT 246;\nINSERT INTO shoes SELECT train_type, center, inspection_time FROM bi.inventory_delta WHERE train_type > 134;\nSQL\n", "labels": {"reads": [{"table": "solana_transactions", "columns": ["sensor_reading", "underrepresented_community"]}, {"table": "bi.inventory_delta", "columns": ["train_type", "center", "inspection_time"]}], "writes": [{"table": "shoes", "columns": ["train_type", "center", "inspection_time"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model sustainability depends on drought_impact\ndbt run --select sustainability --vars '{\"src\":\"drought_impact\"}'\n", "labels": {"reads": [{"table": "drought_impact", "columns": null}], "writes": [{"table": "sustainability", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT neighborhood_id, inspection_id FROM ocean_species\", engine)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"mart.mart_sessions_di\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ocean_species", "columns": ["neighborhood_id", "inspection_id"]}], "writes": [{"table": "mart.mart_sessions_di", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT investment_id, chip_model FROM footwear LIMIT 250;\nINSERT INTO agri_innov SELECT service_id, area_type FROM defense_contracts_v2 WHERE service_id > 118;\nSQL\n", "labels": {"reads": [{"table": "footwear", "columns": ["investment_id", "chip_model"]}, {"table": "defense_contracts_v2", "columns": ["service_id", "area_type"]}], "writes": [{"table": "agri_innov", "columns": ["service_id", "area_type"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table agency_satellites --columns nurse,loan_amount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "agency_satellites", "columns": ["nurse", "loan_amount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT is_organic, all_games FROM artwork_styles\", engine)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"talent_acquisition\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "artwork_styles", "columns": ["is_organic", "all_games"]}], "writes": [{"table": "talent_acquisition", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT building_short_name, assists FROM pilot LIMIT 494\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO oceania_countries SELECT open_year, total_employees FROM therapists WHERE open_year > 53\")\n", "labels": {"reads": [{"table": "pilot", "columns": ["building_short_name", "assists"]}, {"table": "therapists", "columns": ["open_year", "total_employees"]}], "writes": [{"table": "oceania_countries", "columns": ["open_year", "total_employees"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO user_workouts_march SELECT visitorid, voter_id, account_details, departmentname FROM defenseprojects WHERE visitorid > 430;\nEOF\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["visitorid", "voter_id", "account_details", "departmentname"]}], "writes": [{"table": "user_workouts_march", "columns": ["visitorid", "voter_id", "account_details", "departmentname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"grapes\")\nsrc.write.insertInto(\"beauty_products\", overwrite=True)\n", "labels": {"reads": [{"table": "grapes", "columns": null}], "writes": [{"table": "beauty_products", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table farmers_india --target-dir /tmp/land\n", "labels": {"reads": [{"table": "farmers_india", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO film_market_estimation SELECT a.purchaseid, b.firstname FROM certificate a JOIN veterans b ON a.premise_id = b.premise_id\"\n", "labels": {"reads": [{"table": "certificate", "columns": null}, {"table": "veterans", "columns": null}], "writes": [{"table": "film_market_estimation", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainability_metrics SELECT * FROM legacy\ncur.execute(\"SELECT task, famous_release_date FROM military_aircraft_maintenance LIMIT 213\")\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": ["task", "famous_release_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bank_info --columns policy_number,distance --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bank_info", "columns": ["policy_number", "distance"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO education_programs SELECT a.issued_date, b.medium FROM midwest_region a JOIN user_video_view b ON a.song_year = b.song_year\"\n", "labels": {"reads": [{"table": "midwest_region", "columns": null}, {"table": "user_video_view", "columns": null}], "writes": [{"table": "education_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT song_release_year, session_date FROM water_sources LIMIT 314;\nINSERT INTO ads.ads_payments_hourly SELECT avg_usage, employeename FROM biosensor.patents WHERE avg_usage > 408;\nSQL\n", "labels": {"reads": [{"table": "water_sources", "columns": ["song_release_year", "session_date"]}, {"table": "biosensor.patents", "columns": ["avg_usage", "employeename"]}], "writes": [{"table": "ads.ads_payments_hourly", "columns": ["avg_usage", "employeename"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dysprosiumproduction (provider, zip_code) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dysprosiumproduction", "columns": ["provider", "zip_code"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO donationsbycause (copy_number, policy) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "donationsbycause", "columns": ["copy_number", "policy"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO concert SELECT a.menu_category, b.concert_id FROM hotel_revenue a JOIN view_unit_status b ON a.mission_name = b.mission_name\"\n", "labels": {"reads": [{"table": "hotel_revenue", "columns": null}, {"table": "view_unit_status", "columns": null}], "writes": [{"table": "concert", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO union_members SELECT statement_details, programname FROM book WHERE statement_details > 486\"], check=True)\n", "labels": {"reads": [{"table": "book", "columns": ["statement_details", "programname"]}], "writes": [{"table": "union_members", "columns": ["statement_details", "programname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.beds > 192).all()\n# src table: community_development_projects\nengine.execute(\"INSERT INTO buildings SELECT * FROM community_development_projects\")\n", "labels": {"reads": [{"table": "community_development_projects", "columns": null}], "writes": [{"table": "buildings", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM parts\"\n", "labels": {"reads": [{"table": "parts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM drilling_rigs\"\n", "labels": {"reads": [{"table": "drilling_rigs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT tot_cred, aircraft_id FROM eventdates LIMIT 375;\nINSERT INTO outcomes SELECT bioreactor_id, max_depth, is_valid FROM bi.bi_orders_daily WHERE bioreactor_id > 343;\nSQL\n", "labels": {"reads": [{"table": "eventdates", "columns": ["tot_cred", "aircraft_id"]}, {"table": "bi.bi_orders_daily", "columns": ["bioreactor_id", "max_depth", "is_valid"]}], "writes": [{"table": "outcomes", "columns": ["bioreactor_id", "max_depth", "is_valid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO refugee_support (community_id, attorney) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "refugee_support", "columns": ["community_id", "attorney"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO trust SELECT donation_amount, vulnerability, membership_id FROM production_data WHERE donation_amount > 482\"\n", "labels": {"reads": [{"table": "production_data", "columns": ["donation_amount", "vulnerability", "membership_id"]}], "writes": [{"table": "trust", "columns": ["donation_amount", "vulnerability", "membership_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wrestler\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "wrestler", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO problem_log SELECT a.wastetype, b.restaurantid FROM smartcities a JOIN drought_data b ON a.bandmateid = b.bandmateid\"\n", "labels": {"reads": [{"table": "smartcities", "columns": null}, {"table": "drought_data", "columns": null}], "writes": [{"table": "problem_log", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"sustainabilityratings\")\npersist_to_target(df, \"reservoirs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "sustainabilityratings", "columns": null}], "writes": [{"table": "reservoirs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"disabilitysupportprograms\").toPandas()\ndf[[\"year_deforested\", \"deliveryaddress\"]].to_sql(\"australia_offset_programs\", engine, index=False)\n", "labels": {"reads": [{"table": "disabilitysupportprograms", "columns": null}], "writes": [{"table": "australia_offset_programs", "columns": ["year_deforested", "deliveryaddress"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO criminalcases SELECT incidenttype, founded, refugee_id, license_number FROM course WHERE incidenttype > 89\")\n", "labels": {"reads": [{"table": "course", "columns": ["incidenttype", "founded", "refugee_id", "license_number"]}], "writes": [{"table": "criminalcases", "columns": ["incidenttype", "founded", "refugee_id", "license_number"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"solar_farms\")\nupsert_to_store(df, \"contract_states\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "solar_farms", "columns": null}], "writes": [{"table": "contract_states", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"dws_products\")\npersist_to_warehouse(df, \"vulnerabilities\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws_products", "columns": null}], "writes": [{"table": "vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO smartcontracts SELECT a.customer_type_code, b.online_dispute_resolution FROM fertilizer a JOIN mart_exposure_hourly b ON a.lastdonationdate = b.lastdonationdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fertilizer", "columns": null}, {"table": "mart_exposure_hourly", "columns": null}], "writes": [{"table": "smartcontracts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO renewable_energy_investments (volume, destinationid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "renewable_energy_investments", "columns": ["volume", "destinationid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dwd_payments_delta SELECT number_of_vessels, sensor_id, working_horses FROM item WHERE number_of_vessels > 125\"\n", "labels": {"reads": [{"table": "item", "columns": ["number_of_vessels", "sensor_id", "working_horses"]}], "writes": [{"table": "dwd_payments_delta", "columns": ["number_of_vessels", "sensor_id", "working_horses"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"epl_teams\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"assignedto\")\n", "labels": {"reads": [{"table": "epl_teams", "columns": null}], "writes": [{"table": "assignedto", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT start_station_name, rental_rate FROM miningdepartment\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"esportsteamsafrica\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "miningdepartment", "columns": ["start_station_name", "rental_rate"]}], "writes": [{"table": "esportsteamsafrica", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO daily_revenue SELECT speed, contract_end_date, student_details, log_entry_date FROM video_content WHERE speed > 416;\nEOF\n", "labels": {"reads": [{"table": "video_content", "columns": ["speed", "contract_end_date", "student_details", "log_entry_date"]}], "writes": [{"table": "daily_revenue", "columns": ["speed", "contract_end_date", "student_details", "log_entry_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"publication\")\nwrite_to_output(df, \"dwd.dwd_cart_item_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "publication", "columns": null}], "writes": [{"table": "dwd.dwd_cart_item_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO humanitarian_operations SELECT elimination_move, transaction_date FROM excavation_sites WHERE elimination_move > 194\"\n", "labels": {"reads": [{"table": "excavation_sites", "columns": ["elimination_move", "transaction_date"]}], "writes": [{"table": "humanitarian_operations", "columns": ["elimination_move", "transaction_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO southeast_providers SELECT apt_number, acidification_level, cultural_diversity FROM machinery WHERE apt_number > 438\")\n", "labels": {"reads": [{"table": "machinery", "columns": ["apt_number", "acidification_level", "cultural_diversity"]}], "writes": [{"table": "southeast_providers", "columns": ["apt_number", "acidification_level", "cultural_diversity"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO player_sessions SELECT shipment_date, unionid FROM indie_artists WHERE shipment_date > 451\"\n", "labels": {"reads": [{"table": "indie_artists", "columns": ["shipment_date", "unionid"]}], "writes": [{"table": "player_sessions", "columns": ["shipment_date", "unionid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO accounts SELECT * FROM legacy\ncur.execute(\"SELECT stockid, method_name FROM characteristics LIMIT 371\")\n", "labels": {"reads": [{"table": "characteristics", "columns": ["stockid", "method_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"fabrics\")\npush_to_output(df, \"habitat\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fabrics", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO mentalhealthprofessional SELECT sustainability_rating, department_id, product_category, exoplanet FROM atlantic_ocean WHERE sustainability_rating > 117\")\n", "labels": {"reads": [{"table": "atlantic_ocean", "columns": ["sustainability_rating", "department_id", "product_category", "exoplanet"]}], "writes": [{"table": "mentalhealthprofessional", "columns": ["sustainability_rating", "department_id", "product_category", "exoplanet"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads.ads_users_hourly SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM diversification_projects\"\n", "labels": {"reads": [{"table": "diversification_projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table wedding --target-dir /tmp/land\n", "labels": {"reads": [{"table": "wedding", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table medicine_enzyme_interaction --columns farmer_id,vin --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "medicine_enzyme_interaction", "columns": ["farmer_id", "vin"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table carbon_offsets.carbon_offsets --columns draft_details,catalog_level_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "carbon_offsets.carbon_offsets", "columns": ["draft_details", "catalog_level_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO diversity_metrics SELECT season_number, undergraduate, museum_name, away_team_score FROM thefts WHERE season_number > 109\"\n", "labels": {"reads": [{"table": "thefts", "columns": ["season_number", "undergraduate", "museum_name", "away_team_score"]}], "writes": [{"table": "diversity_metrics", "columns": ["season_number", "undergraduate", "museum_name", "away_team_score"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT programoutcomeid, fuelconsumed FROM productsafety LIMIT 376;\nINSERT INTO unions SELECT party_name, dlocation, problem_log_id FROM ads_coupon_use_full WHERE party_name > 396;\nSQL\n", "labels": {"reads": [{"table": "productsafety", "columns": ["programoutcomeid", "fuelconsumed"]}, {"table": "ads_coupon_use_full", "columns": ["party_name", "dlocation", "problem_log_id"]}], "writes": [{"table": "unions", "columns": ["party_name", "dlocation", "problem_log_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table catalogs --target-dir /tmp/land\n", "labels": {"reads": [{"table": "catalogs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM assets\"\n", "labels": {"reads": [{"table": "assets", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"satellite_missions_large\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_member_point_di\")\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": null}], "writes": [{"table": "dws.dws_member_point_di", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT addressid, resource_id FROM stations LIMIT 61\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "stations", "columns": ["addressid", "resource_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stores_2\").toPandas()\ndf[[\"garment_id\", \"crop_name\"]].to_sql(\"militarydrones\", engine, index=False)\n", "labels": {"reads": [{"table": "stores_2", "columns": null}], "writes": [{"table": "militarydrones", "columns": ["garment_id", "crop_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT last_updated, sqft FROM call_volume LIMIT 243;\nINSERT INTO ads_exposure_hourly SELECT dorm_name, storename FROM ads.ads_inventory_df WHERE dorm_name > 393;\nSQL\n", "labels": {"reads": [{"table": "call_volume", "columns": ["last_updated", "sqft"]}, {"table": "ads.ads_inventory_df", "columns": ["dorm_name", "storename"]}], "writes": [{"table": "ads_exposure_hourly", "columns": ["dorm_name", "storename"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.health_equity_metric_3 > 314).all()\n# src table: restaurant_revenue\nengine.execute(\"INSERT INTO medicine SELECT * FROM restaurant_revenue\")\n", "labels": {"reads": [{"table": "restaurant_revenue", "columns": null}], "writes": [{"table": "medicine", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO competition SELECT silver, sport, contract_count FROM ads.ads_member_point_daily WHERE silver > 197\"\n", "labels": {"reads": [{"table": "ads.ads_member_point_daily", "columns": ["silver", "sport", "contract_count"]}], "writes": [{"table": "competition", "columns": ["silver", "sport", "contract_count"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"average\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"carbon_sequestration\")\n", "labels": {"reads": [{"table": "average", "columns": null}], "writes": [{"table": "carbon_sequestration", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM biodiversity\"\n", "labels": {"reads": [{"table": "biodiversity", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.member_in_charge_id > 294).all()\n# src table: habitats\nengine.execute(\"INSERT INTO dorm SELECT * FROM habitats\")\n", "labels": {"reads": [{"table": "habitats", "columns": null}], "writes": [{"table": "dorm", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO levees (farmland_id, prepnurse) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "levees", "columns": ["farmland_id", "prepnurse"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO show SELECT time_year, sustainability_id, lettergrade FROM dwd.dwd_payments WHERE time_year > 384\"\n", "labels": {"reads": [{"table": "dwd.dwd_payments", "columns": ["time_year", "sustainability_id", "lettergrade"]}], "writes": [{"table": "show", "columns": ["time_year", "sustainability_id", "lettergrade"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO local_impact_japan SELECT a.prereq_id, b.fertilizer_id FROM manufacturing_processes a JOIN fossil_fuel_vehicles_japan b ON a.dphone = b.dphone\"\n", "labels": {"reads": [{"table": "manufacturing_processes", "columns": null}, {"table": "fossil_fuel_vehicles_japan", "columns": null}], "writes": [{"table": "local_impact_japan", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO infrastructureprojects SELECT video_id, sodium, pname FROM sanctuaryanimals WHERE video_id > 398\"\n", "labels": {"reads": [{"table": "sanctuaryanimals", "columns": ["video_id", "sodium", "pname"]}], "writes": [{"table": "infrastructureprojects", "columns": ["video_id", "sodium", "pname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_resources\").toPandas()\ndf[[\"service_id\", \"q1_2022_views\"]].to_sql(\"sustainableprojects\", engine, index=False)\n", "labels": {"reads": [{"table": "rural_resources", "columns": null}], "writes": [{"table": "sustainableprojects", "columns": ["service_id", "q1_2022_views"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"prereq\").toPandas()\ndf[[\"entrydate\", \"species_id\"]].to_sql(\"rural_areas\", engine, index=False)\n", "labels": {"reads": [{"table": "prereq", "columns": null}], "writes": [{"table": "rural_areas", "columns": ["entrydate", "species_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO film_actor SELECT explainability_score, ad_type, healthequitymetricscore, train_type FROM station_emergencies WHERE explainability_score > 329\"\n", "labels": {"reads": [{"table": "station_emergencies", "columns": ["explainability_score", "ad_type", "healthequitymetricscore", "train_type"]}], "writes": [{"table": "film_actor", "columns": ["explainability_score", "ad_type", "healthequitymetricscore", "train_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wildlife\").toPandas()\ndf[[\"restaurantname\", \"area_ha\"]].to_sql(\"defense_project_timelines\", engine, index=False)\n", "labels": {"reads": [{"table": "wildlife", "columns": null}], "writes": [{"table": "defense_project_timelines", "columns": ["restaurantname", "area_ha"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT participated_in_open_pedagogy, game_id FROM brand_info LIMIT 138;\nINSERT INTO part_faults SELECT operating_system, transact_count, product_subcategory, gdp FROM bi.events_df WHERE operating_system > 269;\nSQL\n", "labels": {"reads": [{"table": "brand_info", "columns": ["participated_in_open_pedagogy", "game_id"]}, {"table": "bi.events_df", "columns": ["operating_system", "transact_count", "product_subcategory", "gdp"]}], "writes": [{"table": "part_faults", "columns": ["operating_system", "transact_count", "product_subcategory", "gdp"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climate_investments (mappingname, sensor_reading) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climate_investments", "columns": ["mappingname", "sensor_reading"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT schedule, calendar_date FROM historicalcontexts LIMIT 370\")\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO streams SELECT fare_amount, ingredient FROM support WHERE fare_amount > 407\")\n", "labels": {"reads": [{"table": "historicalcontexts", "columns": ["schedule", "calendar_date"]}, {"table": "support", "columns": ["fare_amount", "ingredient"]}], "writes": [{"table": "streams", "columns": ["fare_amount", "ingredient"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT num_of_stock, fabricid FROM bi_refunds_daily LIMIT 5\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "bi_refunds_daily", "columns": ["num_of_stock", "fabricid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO spacemissions (killed, customer_details) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "spacemissions", "columns": ["killed", "customer_details"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT fabrictype, payment_method FROM mart.mart_users\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"mine\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_users", "columns": ["fabrictype", "payment_method"]}], "writes": [{"table": "mine", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT emp_jobcode, client_id FROM activity LIMIT 496;\nINSERT INTO catalog_contents_additional_attributes SELECT maintenance_contract_id, fuelid, train_type, all_home FROM vehicle_prices WHERE maintenance_contract_id > 426;\nSQL\n", "labels": {"reads": [{"table": "activity", "columns": ["emp_jobcode", "client_id"]}, {"table": "vehicle_prices", "columns": ["maintenance_contract_id", "fuelid", "train_type", "all_home"]}], "writes": [{"table": "catalog_contents_additional_attributes", "columns": ["maintenance_contract_id", "fuelid", "train_type", "all_home"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model tourism_activities depends on rnd_budget\ndbt build --select tourism_activities --vars 'source: rnd_budget'\n", "labels": {"reads": [{"table": "rnd_budget", "columns": null}], "writes": [{"table": "tourism_activities", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO trends_2022 (is_compliant, investment) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "trends_2022", "columns": ["is_compliant", "investment"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"music_streaming\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artifacts\")\n", "labels": {"reads": [{"table": "music_streaming", "columns": null}], "writes": [{"table": "artifacts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainable_fabrics SELECT * FROM legacy\ncur.execute(\"SELECT vesselname, event_count FROM algorithmic_fairness LIMIT 480\")\n", "labels": {"reads": [{"table": "algorithmic_fairness", "columns": ["vesselname", "event_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"peacekeeping_units\").toPandas()\ndf[[\"state_province\", \"stu_gpa\"]].to_sql(\"exit\", engine, index=False)\n", "labels": {"reads": [{"table": "peacekeeping_units", "columns": null}], "writes": [{"table": "exit", "columns": ["state_province", "stu_gpa"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"restorative_justice_sentences\")\nupsert_to_warehouse(df, \"stg.stg_products_delta\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "restorative_justice_sentences", "columns": null}], "writes": [{"table": "stg.stg_products_delta", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO subway SELECT rank, contractor FROM railway WHERE rank > 265\")\n", "labels": {"reads": [{"table": "railway", "columns": ["rank", "contractor"]}], "writes": [{"table": "subway", "columns": ["rank", "contractor"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO green_certification SELECT played, membership_id FROM dws.cart_item_full WHERE played > 86\"\n", "labels": {"reads": [{"table": "dws.cart_item_full", "columns": ["played", "membership_id"]}], "writes": [{"table": "green_certification", "columns": ["played", "membership_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT vehicleid, address_id FROM asia_events LIMIT 261;\nINSERT INTO party_events SELECT plan_id, resolution, pollutant_type, green_building_id FROM vessel WHERE plan_id > 384;\nSQL\n", "labels": {"reads": [{"table": "asia_events", "columns": ["vehicleid", "address_id"]}, {"table": "vessel", "columns": ["plan_id", "resolution", "pollutant_type", "green_building_id"]}], "writes": [{"table": "party_events", "columns": ["plan_id", "resolution", "pollutant_type", "green_building_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO recyclingprogram SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO salary SELECT next_entry_id, tripdatetime, date_of_ceremony FROM product_characteristics WHERE next_entry_id > 431;\nEOF\n", "labels": {"reads": [{"table": "product_characteristics", "columns": ["next_entry_id", "tripdatetime", "date_of_ceremony"]}], "writes": [{"table": "salary", "columns": ["next_entry_id", "tripdatetime", "date_of_ceremony"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO mexico_regions SELECT founder_race, truck_details FROM projects WHERE founder_race > 204\"\n", "labels": {"reads": [{"table": "projects", "columns": ["founder_race", "truck_details"]}], "writes": [{"table": "mexico_regions", "columns": ["founder_race", "truck_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT membership_type, velocity FROM companies_extended LIMIT 26\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "companies_extended", "columns": ["membership_type", "velocity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model transaction depends on green_building_projects\ndbt build --models transaction --vars '{\"source_table\":\"green_building_projects\"}'\n", "labels": {"reads": [{"table": "green_building_projects", "columns": null}], "writes": [{"table": "transaction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table volume --target-dir /tmp/land\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM artifact_analysis\"\n", "labels": {"reads": [{"table": "artifact_analysis", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table tracklists --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tracklists", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model safe_dataset depends on open_pedagogy_enrollment\ndbt build --models safe_dataset --vars 'source: open_pedagogy_enrollment'\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}], "writes": [{"table": "safe_dataset", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO language SELECT a.attraction_type_description, b.resident_id FROM factory_connections a JOIN stg.stg_events_hourly b ON a.fish_population = b.fish_population\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "factory_connections", "columns": null}, {"table": "stg.stg_events_hourly", "columns": null}], "writes": [{"table": "language", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO environmental_impact_stats SELECT student, depth, asset_disposed_date FROM functional_areas WHERE student > 346\"\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["student", "depth", "asset_disposed_date"]}], "writes": [{"table": "environmental_impact_stats", "columns": ["student", "depth", "asset_disposed_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_source(ctx, \"dwd.dwd_products\")\npush_to_store(df, \"disabilitysupportprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": null}], "writes": [{"table": "disabilitysupportprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dispensarysales\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"communitycourtcases\")\n", "labels": {"reads": [{"table": "dispensarysales", "columns": null}], "writes": [{"table": "communitycourtcases", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO diseases SELECT * FROM legacy\ncur.execute(\"SELECT document_code, typeid FROM sites LIMIT 60\")\n", "labels": {"reads": [{"table": "sites", "columns": ["document_code", "typeid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_events_di SELECT a.speed, b.valuation FROM bi.products_daily a JOIN roller_coaster b ON a.observation_id = b.observation_id\"\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": null}, {"table": "roller_coaster", "columns": null}], "writes": [{"table": "mart.mart_events_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM arcticocean\"\n", "labels": {"reads": [{"table": "arcticocean", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO geological_survey SELECT license_plate, safety_record, local, show_name FROM recycledmaterialsgarments WHERE license_plate > 368\"\n", "labels": {"reads": [{"table": "recycledmaterialsgarments", "columns": ["license_plate", "safety_record", "local", "show_name"]}], "writes": [{"table": "geological_survey", "columns": ["license_plate", "safety_record", "local", "show_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sustainability_fact SELECT a.pricepergram, b.assistingnurse FROM chemical_production_3 a JOIN mart.mart_payments_df b ON a.tour_id = b.tour_id\"\n", "labels": {"reads": [{"table": "chemical_production_3", "columns": null}, {"table": "mart.mart_payments_df", "columns": null}], "writes": [{"table": "sustainability_fact", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.clicks\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.clicks", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO seamounts SELECT algorithm, sustainability_id, price_in_euros, unit FROM inclusion_efforts WHERE algorithm > 457\")\n", "labels": {"reads": [{"table": "inclusion_efforts", "columns": ["algorithm", "sustainability_id", "price_in_euros", "unit"]}], "writes": [{"table": "seamounts", "columns": ["algorithm", "sustainability_id", "price_in_euros", "unit"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table employee_demographics --columns condition_id,therapy_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "employee_demographics", "columns": ["condition_id", "therapy_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO seafoodsouthafricakenya SELECT artist_name, researcher, hours_played, launch_id FROM weather WHERE artist_name > 472\"\n", "labels": {"reads": [{"table": "weather", "columns": ["artist_name", "researcher", "hours_played", "launch_id"]}], "writes": [{"table": "seafoodsouthafricakenya", "columns": ["artist_name", "researcher", "hours_played", "launch_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO employees SELECT show_name, ratingdate FROM dailyapplestreams WHERE show_name > 472\"], check=True)\n", "labels": {"reads": [{"table": "dailyapplestreams", "columns": ["show_name", "ratingdate"]}], "writes": [{"table": "employees", "columns": ["show_name", "ratingdate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dysprosium_mines SELECT hourlyrate, recipient_id, floors, tree_species FROM member_details WHERE hourlyrate > 350\"\n", "labels": {"reads": [{"table": "member_details", "columns": ["hourlyrate", "recipient_id", "floors", "tree_species"]}], "writes": [{"table": "dysprosium_mines", "columns": ["hourlyrate", "recipient_id", "floors", "tree_species"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT policy_holder_id, chemicalid FROM latam_schema.education_budget LIMIT 257;\nINSERT INTO energy_efficiency_projects SELECT target_id, fund_name FROM defense_projects WHERE target_id > 357;\nSQL\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": ["policy_holder_id", "chemicalid"]}, {"table": "defense_projects", "columns": ["target_id", "fund_name"]}], "writes": [{"table": "energy_efficiency_projects", "columns": ["target_id", "fund_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO vehicle_sales SELECT * FROM legacy\ncur.execute(\"SELECT voter_id, content_type FROM labour_productivity LIMIT 141\")\n", "labels": {"reads": [{"table": "labour_productivity", "columns": ["voter_id", "content_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table whale_sightings --target-dir /tmp/land\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT offender_name, attendee_race FROM region_stats LIMIT 447\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO sustainable_urban_properties_2 SELECT invoice_date, component_name, attraction_name, volunteer_year FROM employeepromotions WHERE invoice_date > 113\")\n", "labels": {"reads": [{"table": "region_stats", "columns": ["offender_name", "attendee_race"]}, {"table": "employeepromotions", "columns": ["invoice_date", "component_name", "attraction_name", "volunteer_year"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["invoice_date", "component_name", "attraction_name", "volunteer_year"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"broadband_customers_global\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ethicalaibudget\")\n", "labels": {"reads": [{"table": "broadband_customers_global", "columns": null}], "writes": [{"table": "ethicalaibudget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT min_age, meal_name FROM ods.vendors_di LIMIT 253\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "ods.vendors_di", "columns": ["min_age", "meal_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO crop_temperature SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO pets SELECT openingid, directed_by, mentalhealthscore FROM event_attendance WHERE openingid > 370\"\n", "labels": {"reads": [{"table": "event_attendance", "columns": ["openingid", "directed_by", "mentalhealthscore"]}], "writes": [{"table": "pets", "columns": ["openingid", "directed_by", "mentalhealthscore"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO articles SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"platform\")\npush_to_sink(df, \"healthequitymetrics\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "platform", "columns": null}], "writes": [{"table": "healthequitymetrics", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO exhibition_record SELECT well_depth, organisation_id, agency_id FROM asia_events WHERE well_depth > 296\")\n", "labels": {"reads": [{"table": "asia_events", "columns": ["well_depth", "organisation_id", "agency_id"]}], "writes": [{"table": "exhibition_record", "columns": ["well_depth", "organisation_id", "agency_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM genetics_stats.research_projects\", conn)\ndf.to_sql(\"habitat\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "genetics_stats.research_projects", "columns": null}], "writes": [{"table": "habitat", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO eventparticipation SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO charging_stations SELECT resolution, date_of_latest_revision, location FROM florida_conservation_initiatives WHERE resolution > 269\"\n", "labels": {"reads": [{"table": "florida_conservation_initiatives", "columns": ["resolution", "date_of_latest_revision", "location"]}], "writes": [{"table": "charging_stations", "columns": ["resolution", "date_of_latest_revision", "location"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table appliances --target-dir /tmp/land\n", "labels": {"reads": [{"table": "appliances", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT weight, healthcareid FROM sustainable_menu_items LIMIT 472\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "sustainable_menu_items", "columns": ["weight", "healthcareid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO disease_prevalence (coupon_id, reported) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "disease_prevalence", "columns": ["coupon_id", "reported"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO food_justice_orgs SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT investment_id, line_name FROM veteran_occupations\", engine)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"us_military_personnel\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "veteran_occupations", "columns": ["investment_id", "line_name"]}], "writes": [{"table": "us_military_personnel", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO affordablehousing SELECT user_account, system_name, email_address FROM dailystreams WHERE user_account > 478\"\n", "labels": {"reads": [{"table": "dailystreams", "columns": ["user_account", "system_name", "email_address"]}], "writes": [{"table": "affordablehousing", "columns": ["user_account", "system_name", "email_address"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO carbon_prices SELECT * FROM legacy\ncur.execute(\"SELECT vehicle_flight_number, make FROM oil_production LIMIT 195\")\n", "labels": {"reads": [{"table": "oil_production", "columns": ["vehicle_flight_number", "make"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table streams --columns player_id,cargoid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "streams", "columns": ["player_id", "cargoid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT university_type, vehicle_model FROM france_culture LIMIT 77\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO securityincidents SELECT centerid, contract_start_date, quantity FROM trenches WHERE centerid > 113\")\n", "labels": {"reads": [{"table": "france_culture", "columns": ["university_type", "vehicle_model"]}, {"table": "trenches", "columns": ["centerid", "contract_start_date", "quantity"]}], "writes": [{"table": "securityincidents", "columns": ["centerid", "contract_start_date", "quantity"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM all_star\", conn)\ndf.to_sql(\"dancefunding\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "all_star", "columns": null}], "writes": [{"table": "dancefunding", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT participation_date, firstname FROM customer_size_diversity\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"safetytesting\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customer_size_diversity", "columns": ["participation_date", "firstname"]}], "writes": [{"table": "safetytesting", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table carbon_emissions --columns archaeologist_id,team_id_loser --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "carbon_emissions", "columns": ["archaeologist_id", "team_id_loser"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT agency_id, violation_type FROM artcollection LIMIT 239;\nINSERT INTO daily_industrial_water_usage SELECT donationid, phone_number, sensor_type, playlist_id FROM renewable_energy_projects WHERE donationid > 19;\nSQL\n", "labels": {"reads": [{"table": "artcollection", "columns": ["agency_id", "violation_type"]}, {"table": "renewable_energy_projects", "columns": ["donationid", "phone_number", "sensor_type", "playlist_id"]}], "writes": [{"table": "daily_industrial_water_usage", "columns": ["donationid", "phone_number", "sensor_type", "playlist_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO exhibition_visits SELECT * FROM legacy\ncur.execute(\"SELECT home_team_score, score FROM vrgames LIMIT 205\")\n", "labels": {"reads": [{"table": "vrgames", "columns": ["home_team_score", "score"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT actual_order_id, special_features FROM co2emissions LIMIT 81\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "co2emissions", "columns": ["actual_order_id", "special_features"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM all_star\", conn)\ndf.to_sql(\"dwd.dwd_inventory_hourly\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "all_star", "columns": null}], "writes": [{"table": "dwd.dwd_inventory_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM heritagesites\", conn)\ndf.to_sql(\"militarypersonnel\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "heritagesites", "columns": null}], "writes": [{"table": "militarypersonnel", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mountain SELECT patient_age, build_date, is_ev FROM oceania_countries WHERE patient_age > 126\"\n", "labels": {"reads": [{"table": "oceania_countries", "columns": ["patient_age", "build_date", "is_ev"]}], "writes": [{"table": "mountain", "columns": ["patient_age", "build_date", "is_ev"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO students SELECT supplier_id, amount_paid, biz_date FROM defense_diplomacy WHERE supplier_id > 221\")\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": ["supplier_id", "amount_paid", "biz_date"]}], "writes": [{"table": "students", "columns": ["supplier_id", "amount_paid", "biz_date"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dorm_amenity depends on seafood\ndbt build -s dorm_amenity --vars '{\"source_table\":\"seafood\"}'\n", "labels": {"reads": [{"table": "seafood", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO port_visits (fund_name, tasktype) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "port_visits", "columns": ["fund_name", "tasktype"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO cosmetics SELECT last_checkup_date, market_value, form_type_code FROM mart_refunds_delta WHERE last_checkup_date > 134\")\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": ["last_checkup_date", "market_value", "form_type_code"]}], "writes": [{"table": "cosmetics", "columns": ["last_checkup_date", "market_value", "form_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO gradeconversion SELECT eliminated_by, number_of_platforms, supply_volume FROM trip WHERE eliminated_by > 490\"], check=True)\n", "labels": {"reads": [{"table": "trip", "columns": ["eliminated_by", "number_of_platforms", "supply_volume"]}], "writes": [{"table": "gradeconversion", "columns": ["eliminated_by", "number_of_platforms", "supply_volume"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO member_attendance SELECT a.vice_president_vote, b.sponsor_name FROM innovation_projects a JOIN donationsbycause b ON a.sales_in_billion = b.sales_in_billion\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "innovation_projects", "columns": null}, {"table": "donationsbycause", "columns": null}], "writes": [{"table": "member_attendance", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"excavation_sites\")\npersist_to_store(df, \"jobs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "excavation_sites", "columns": null}], "writes": [{"table": "jobs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO therapy SELECT song, trackid, host FROM asteroids WHERE song > 222\"\n", "labels": {"reads": [{"table": "asteroids", "columns": ["song", "trackid", "host"]}], "writes": [{"table": "therapy", "columns": ["song", "trackid", "host"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO intel_ops SELECT accelerator_id, satelliteid, resolved, decision FROM militaryinnovations WHERE accelerator_id > 90\"\n", "labels": {"reads": [{"table": "militaryinnovations", "columns": ["accelerator_id", "satelliteid", "resolved", "decision"]}], "writes": [{"table": "intel_ops", "columns": ["accelerator_id", "satelliteid", "resolved", "decision"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO organicproducts SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads_vendors_hourly SELECT ingredient, trial_name, purchase_details, publisher FROM rural_infrastructure WHERE ingredient > 392\"], check=True)\n", "labels": {"reads": [{"table": "rural_infrastructure", "columns": ["ingredient", "trial_name", "purchase_details", "publisher"]}], "writes": [{"table": "ads_vendors_hourly", "columns": ["ingredient", "trial_name", "purchase_details", "publisher"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT employee_count, paper_id FROM heart_rate_data\", engine)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\ndf.to_sql(\"department\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "heart_rate_data", "columns": ["employee_count", "paper_id"]}], "writes": [{"table": "department", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO public.crime_types SELECT mean_sea_level_pressure_inches, shelter_name, permit_date, digital_asset FROM service_budget WHERE mean_sea_level_pressure_inches > 285\"\n", "labels": {"reads": [{"table": "service_budget", "columns": ["mean_sea_level_pressure_inches", "shelter_name", "permit_date", "digital_asset"]}], "writes": [{"table": "public.crime_types", "columns": ["mean_sea_level_pressure_inches", "shelter_name", "permit_date", "digital_asset"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO maintenance_engineers SELECT building, film_id, inspection_time FROM carbon_prices_3 WHERE building > 159;\nEOF\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["building", "film_id", "inspection_time"]}], "writes": [{"table": "maintenance_engineers", "columns": ["building", "film_id", "inspection_time"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT founders, studentname FROM shop LIMIT 88;\nINSERT INTO research SELECT deliveryid, meal_name, founder_count FROM measurements WHERE deliveryid > 244;\nSQL\n", "labels": {"reads": [{"table": "shop", "columns": ["founders", "studentname"]}, {"table": "measurements", "columns": ["deliveryid", "meal_name", "founder_count"]}], "writes": [{"table": "research", "columns": ["deliveryid", "meal_name", "founder_count"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM customer_address_history\"\n", "labels": {"reads": [{"table": "customer_address_history", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO sectors SELECT a.contract_start, b.contract_amount FROM article_views a JOIN dwd.dwd_vendors b ON a.cust_name = b.cust_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "article_views", "columns": null}, {"table": "dwd.dwd_vendors", "columns": null}], "writes": [{"table": "sectors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO attorney_billing_rates (document_structure_code, cruelty_free) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "attorney_billing_rates", "columns": ["document_structure_code", "cruelty_free"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model recyclingrates depends on shipment\ndbt build -s recyclingrates --vars '{\"src\":\"shipment\"}'\n", "labels": {"reads": [{"table": "shipment", "columns": null}], "writes": [{"table": "recyclingrates", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO threat_severity (visit_date, local) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "threat_severity", "columns": ["visit_date", "local"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"train\").toPandas()\ndf[[\"city_town\", \"volunteer_hours\"]].to_sql(\"playergamedata\", engine, index=False)\n", "labels": {"reads": [{"table": "train", "columns": null}], "writes": [{"table": "playergamedata", "columns": ["city_town", "volunteer_hours"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO erc20_transactions SELECT fish_id, working_horses FROM facility WHERE fish_id > 220\"\n", "labels": {"reads": [{"table": "facility", "columns": ["fish_id", "working_horses"]}], "writes": [{"table": "erc20_transactions", "columns": ["fish_id", "working_horses"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nsql = \"INSERT INTO ocean_acidification_antarctic SELECT a.statement_id, b.sustainability_certified FROM startup_founders a JOIN ocean_health_monitor b ON a.agreementid = b.agreementid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "startup_founders", "columns": null}, {"table": "ocean_health_monitor", "columns": null}], "writes": [{"table": "ocean_acidification_antarctic", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table zipcodes --target-dir /tmp/land\n", "labels": {"reads": [{"table": "zipcodes", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT railway_id, asset_make FROM vaccine_administered LIMIT 360\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "vaccine_administered", "columns": ["railway_id", "asset_make"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO enzyme SELECT room_count, year_opened FROM dwd.dwd_exposure_df WHERE room_count > 137\"], check=True)\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_df", "columns": ["room_count", "year_opened"]}], "writes": [{"table": "enzyme", "columns": ["room_count", "year_opened"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO urbanagricrop SELECT enrollment_date, marketing_region_descriptrion, savingsid, exhibitioncountry FROM cerium_production WHERE enrollment_date > 132;\nEOF\n", "labels": {"reads": [{"table": "cerium_production", "columns": ["enrollment_date", "marketing_region_descriptrion", "savingsid", "exhibitioncountry"]}], "writes": [{"table": "urbanagricrop", "columns": ["enrollment_date", "marketing_region_descriptrion", "savingsid", "exhibitioncountry"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO visitor_statistics SELECT primary_conference, trackid, document_type_name, sustainability_certified FROM user_likes WHERE primary_conference > 199;\nEOF\n", "labels": {"reads": [{"table": "user_likes", "columns": ["primary_conference", "trackid", "document_type_name", "sustainability_certified"]}], "writes": [{"table": "visitor_statistics", "columns": ["primary_conference", "trackid", "document_type_name", "sustainability_certified"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"flu_cases\")\nsrc.write.insertInto(\"body_builder\", overwrite=True)\n", "labels": {"reads": [{"table": "flu_cases", "columns": null}], "writes": [{"table": "body_builder", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ethnicity, financial_capability_score FROM docking LIMIT 453\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "docking", "columns": ["ethnicity", "financial_capability_score"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO wastedata SELECT a.sighting_id, b.hours_contributed FROM employment a JOIN diversification_projects b ON a.advocate_id = b.advocate_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "employment", "columns": null}, {"table": "diversification_projects", "columns": null}], "writes": [{"table": "wastedata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO document_functional_areas (game, incidentid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "document_functional_areas", "columns": ["game", "incidentid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT startup_name, menucategory FROM battery_storage\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"branch\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "battery_storage", "columns": ["startup_name", "menucategory"]}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT quantityproduced, assistingnurse FROM cultural_competency_training LIMIT 126;\nINSERT INTO urban_agriculture_initiatives SELECT date_incident_start, sales_transaction_id, church_id, paper_id FROM transport WHERE date_incident_start > 460;\nSQL\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": ["quantityproduced", "assistingnurse"]}, {"table": "transport", "columns": ["date_incident_start", "sales_transaction_id", "church_id", "paper_id"]}], "writes": [{"table": "urban_agriculture_initiatives", "columns": ["date_incident_start", "sales_transaction_id", "church_id", "paper_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO euro_champs_track_field SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table mental_health_center --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mental_health_center", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.player_name > 467).all()\n# src table: stars\nengine.execute(\"INSERT INTO thefttypes SELECT * FROM stars\")\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": [{"table": "thefttypes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO department_publications SELECT countryid, round_amount FROM device_usage WHERE countryid > 315\"\n", "labels": {"reads": [{"table": "device_usage", "columns": ["countryid", "round_amount"]}], "writes": [{"table": "department_publications", "columns": ["countryid", "round_amount"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bridge SELECT mgr_start_date, station_name FROM satisfaction WHERE mgr_start_date > 60\"\n", "labels": {"reads": [{"table": "satisfaction", "columns": ["mgr_start_date", "station_name"]}], "writes": [{"table": "bridge", "columns": ["mgr_start_date", "station_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO marine_species SELECT dysprosium_prod, faculty, mining_operation FROM vesselfuel WHERE dysprosium_prod > 36\"\n", "labels": {"reads": [{"table": "vesselfuel", "columns": ["dysprosium_prod", "faculty", "mining_operation"]}], "writes": [{"table": "marine_species", "columns": ["dysprosium_prod", "faculty", "mining_operation"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO view_product_availability SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO funding_records SELECT position, saleamount, container_count FROM claims_processing_stages WHERE position > 124\"\n", "labels": {"reads": [{"table": "claims_processing_stages", "columns": ["position", "saleamount", "container_count"]}], "writes": [{"table": "funding_records", "columns": ["position", "saleamount", "container_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ca_menu_items depends on labor_unions\ndbt build --models ca_menu_items --vars '{\"src\":\"labor_unions\"}'\n", "labels": {"reads": [{"table": "labor_unions", "columns": null}], "writes": [{"table": "ca_menu_items", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bridges depends on restaurants\ndbt run --select bridges --vars '{\"src\":\"restaurants\"}'\n", "labels": {"reads": [{"table": "restaurants", "columns": null}], "writes": [{"table": "bridges", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT business_name, purchase_id FROM farmer_details LIMIT 214;\nINSERT INTO cybersecurityincidents SELECT flightid, dispensaryid FROM financial_transactions WHERE flightid > 498;\nSQL\n", "labels": {"reads": [{"table": "farmer_details", "columns": ["business_name", "purchase_id"]}, {"table": "financial_transactions", "columns": ["flightid", "dispensaryid"]}], "writes": [{"table": "cybersecurityincidents", "columns": ["flightid", "dispensaryid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stories SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.dws_coupon_use_full SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO maintenance_engineers SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO shipments SELECT * FROM legacy\ncur.execute(\"SELECT port, produceid FROM bi.inventory_daily LIMIT 304\")\n", "labels": {"reads": [{"table": "bi.inventory_daily", "columns": ["port", "produceid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"open_pedagogy_enrollment\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"sales_region\")\n", "labels": {"reads": [{"table": "open_pedagogy_enrollment", "columns": null}], "writes": [{"table": "sales_region", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model stg_clicks_delta depends on ocean\ndbt build --select stg_clicks_delta --vars '{\"source_table\":\"ocean\"}'\n", "labels": {"reads": [{"table": "ocean", "columns": null}], "writes": [{"table": "stg_clicks_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_events_df\").toPandas()\ndf[[\"policyholder_id\", \"ai_powered_features\"]].to_sql(\"models_safety\", engine, index=False)\n", "labels": {"reads": [{"table": "dws_events_df", "columns": null}], "writes": [{"table": "models_safety", "columns": ["policyholder_id", "ai_powered_features"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO building_permits SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.refunds SELECT open_year, routename FROM engineer_skills WHERE open_year > 427\"], check=True)\n", "labels": {"reads": [{"table": "engineer_skills", "columns": ["open_year", "routename"]}], "writes": [{"table": "ads.refunds", "columns": ["open_year", "routename"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO wind_turbines SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"address\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"volunteerprograms\")\n", "labels": {"reads": [{"table": "address", "columns": null}], "writes": [{"table": "volunteerprograms", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT author_id, swimmer_id FROM tencel_sources LIMIT 19;\nINSERT INTO india_ingredient_sourcing SELECT sitename, visitor_count, app_name FROM ticketspending WHERE sitename > 471;\nSQL\n", "labels": {"reads": [{"table": "tencel_sources", "columns": ["author_id", "swimmer_id"]}, {"table": "ticketspending", "columns": ["sitename", "visitor_count", "app_name"]}], "writes": [{"table": "india_ingredient_sourcing", "columns": ["sitename", "visitor_count", "app_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.region > 142).all()\n# src table: tvshows\nengine.execute(\"INSERT INTO smart_contracts_table SELECT * FROM tvshows\")\n", "labels": {"reads": [{"table": "tvshows", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO plots SELECT a.shoe_brand, b.anomaly FROM associatedheritages a JOIN wastewater_plants b ON a.project_id = b.project_id\"\n", "labels": {"reads": [{"table": "associatedheritages", "columns": null}, {"table": "wastewater_plants", "columns": null}], "writes": [{"table": "plots", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table operations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "operations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO playergamedata SELECT * FROM legacy\ncur.execute(\"SELECT sales_channel, passenger_id FROM arctic_research LIMIT 230\")\n", "labels": {"reads": [{"table": "arctic_research", "columns": ["sales_channel", "passenger_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tracklists\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tracklists", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT host_city_id, author_community FROM circuits LIMIT 183\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "circuits", "columns": ["host_city_id", "author_community"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ref_service_types SELECT affiliation, professional_development_programs FROM order_items WHERE affiliation > 325\"\n", "labels": {"reads": [{"table": "order_items", "columns": ["affiliation", "professional_development_programs"]}], "writes": [{"table": "ref_service_types", "columns": ["affiliation", "professional_development_programs"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO casebilling SELECT media_type_id, garment_type, coach_name FROM smartcityprojects WHERE media_type_id > 383\"\n", "labels": {"reads": [{"table": "smartcityprojects", "columns": ["media_type_id", "garment_type", "coach_name"]}], "writes": [{"table": "casebilling", "columns": ["media_type_id", "garment_type", "coach_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.bi_payments SELECT guest_last_name, lawyer_name, tank, museum_name FROM communitycourtcases WHERE guest_last_name > 427\"\n", "labels": {"reads": [{"table": "communitycourtcases", "columns": ["guest_last_name", "lawyer_name", "tank", "museum_name"]}], "writes": [{"table": "bi.bi_payments", "columns": ["guest_last_name", "lawyer_name", "tank", "museum_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"decentralized_apps\")\nsrc.write.insertInto(\"benefits_overpayments\", overwrite=True)\n", "labels": {"reads": [{"table": "decentralized_apps", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table affiliated_with --target-dir /tmp/land\n", "labels": {"reads": [{"table": "affiliated_with", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table tech_workers_union --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO job_change SELECT home_city, mission, artist FROM org_climate_finance WHERE home_city > 299\")\n", "labels": {"reads": [{"table": "org_climate_finance", "columns": ["home_city", "mission", "artist"]}], "writes": [{"table": "job_change", "columns": ["home_city", "mission", "artist"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT assessment_date, customer_phone FROM product_ingredient LIMIT 245\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "product_ingredient", "columns": ["assessment_date", "customer_phone"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.shipments_full SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM criminalcases\", conn)\ndf.to_sql(\"platformh\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "criminalcases", "columns": null}], "writes": [{"table": "platformh", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO product_sales SELECT time_day, participant_details, cause, nutrient_level FROM country_waste_generation WHERE time_day > 147\"\n", "labels": {"reads": [{"table": "country_waste_generation", "columns": ["time_day", "participant_details", "cause", "nutrient_level"]}], "writes": [{"table": "product_sales", "columns": ["time_day", "participant_details", "cause", "nutrient_level"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO indie_artists SELECT access_count, stockid, artwork_id FROM exhibitions WHERE access_count > 445\"\n", "labels": {"reads": [{"table": "exhibitions", "columns": ["access_count", "stockid", "artwork_id"]}], "writes": [{"table": "indie_artists", "columns": ["access_count", "stockid", "artwork_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO recycling_rates_state (attraction_name, sector) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "recycling_rates_state", "columns": ["attraction_name", "sector"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO marine_conservation SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO vrusers (activity_name, production_rate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "vrusers", "columns": ["activity_name", "production_rate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hours_played, lettergrade FROM members LIMIT 368\")\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO libraries SELECT stu_hrs, architect_id FROM genetics.projects WHERE stu_hrs > 149\")\n", "labels": {"reads": [{"table": "members", "columns": ["hours_played", "lettergrade"]}, {"table": "genetics.projects", "columns": ["stu_hrs", "architect_id"]}], "writes": [{"table": "libraries", "columns": ["stu_hrs", "architect_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.fueldate > 145).all()\n# src table: trainings\nengine.execute(\"INSERT INTO miningdepartment SELECT * FROM trainings\")\n", "labels": {"reads": [{"table": "trainings", "columns": null}], "writes": [{"table": "miningdepartment", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO volunteer_hours SELECT date_of_transaction, cruelty_free, booking_start_date, units_owned FROM member_details WHERE date_of_transaction > 232\"], check=True)\n", "labels": {"reads": [{"table": "member_details", "columns": ["date_of_transaction", "cruelty_free", "booking_start_date", "units_owned"]}], "writes": [{"table": "volunteer_hours", "columns": ["date_of_transaction", "cruelty_free", "booking_start_date", "units_owned"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.outcome_code > 120).all()\n# src table: consumer\nengine.execute(\"INSERT INTO refugee_support SELECT * FROM consumer\")\n", "labels": {"reads": [{"table": "consumer", "columns": null}], "writes": [{"table": "refugee_support", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO weights SELECT a.museumname, b.preference_score FROM song a JOIN material_production b ON a.trade_name = b.trade_name\"\n", "labels": {"reads": [{"table": "song", "columns": null}, {"table": "material_production", "columns": null}], "writes": [{"table": "weights", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.facid > 5).all()\n# src table: investment_accounts\nengine.execute(\"INSERT INTO stg.cart_item_full SELECT * FROM investment_accounts\")\n", "labels": {"reads": [{"table": "investment_accounts", "columns": null}], "writes": [{"table": "stg.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO audience_demographics SELECT therapeutic_area, outcome, representative_id FROM human_resources WHERE therapeutic_area > 264\"\n", "labels": {"reads": [{"table": "human_resources", "columns": ["therapeutic_area", "outcome", "representative_id"]}], "writes": [{"table": "audience_demographics", "columns": ["therapeutic_area", "outcome", "representative_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT award, hometeamid FROM smart_contracts LIMIT 235;\nINSERT INTO organisations SELECT address_line_1, opponent_id, hispanic, volunteer_name FROM stg.stg_campaigns WHERE address_line_1 > 299;\nSQL\n", "labels": {"reads": [{"table": "smart_contracts", "columns": ["award", "hometeamid"]}, {"table": "stg.stg_campaigns", "columns": ["address_line_1", "opponent_id", "hispanic", "volunteer_name"]}], "writes": [{"table": "organisations", "columns": ["address_line_1", "opponent_id", "hispanic", "volunteer_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"redundant_billing_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO co2price SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO emergency_calls SELECT round_date, emp_num, openingid, group_name FROM customers_policies WHERE round_date > 170\"], check=True)\n", "labels": {"reads": [{"table": "customers_policies", "columns": ["round_date", "emp_num", "openingid", "group_name"]}], "writes": [{"table": "emergency_calls", "columns": ["round_date", "emp_num", "openingid", "group_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO claims_documents SELECT founded_year, stayid FROM galleries WHERE founded_year > 243\"], check=True)\n", "labels": {"reads": [{"table": "galleries", "columns": ["founded_year", "stayid"]}], "writes": [{"table": "claims_documents", "columns": ["founded_year", "stayid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO school_bus SELECT regional_population, instructor_id FROM india_solar_power WHERE regional_population > 309\"], check=True)\n", "labels": {"reads": [{"table": "india_solar_power", "columns": ["regional_population", "instructor_id"]}], "writes": [{"table": "school_bus", "columns": ["regional_population", "instructor_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"list\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"habitat3\")\n", "labels": {"reads": [{"table": "list", "columns": null}], "writes": [{"table": "habitat3", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"song\")\nwrite_to_sink(df, \"cultural_competency\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "song", "columns": null}], "writes": [{"table": "cultural_competency", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO student_tests_taken SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT transactions, course_completion FROM investments LIMIT 377;\nINSERT INTO course_attendance SELECT is_ev, theatrename, username FROM sustainable_sourcing WHERE is_ev > 472;\nSQL\n", "labels": {"reads": [{"table": "investments", "columns": ["transactions", "course_completion"]}, {"table": "sustainable_sourcing", "columns": ["is_ev", "theatrename", "username"]}], "writes": [{"table": "course_attendance", "columns": ["is_ev", "theatrename", "username"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"topublictransportation\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"defenseprojects\")\n", "labels": {"reads": [{"table": "topublictransportation", "columns": null}], "writes": [{"table": "defenseprojects", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"party_host\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"danceevents\")\n", "labels": {"reads": [{"table": "party_host", "columns": null}], "writes": [{"table": "danceevents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO rd_expenditure SELECT app_id, trip_distance FROM contractnegotiations WHERE app_id > 124\")\n", "labels": {"reads": [{"table": "contractnegotiations", "columns": ["app_id", "trip_distance"]}], "writes": [{"table": "rd_expenditure", "columns": ["app_id", "trip_distance"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT daily_visitors, address FROM danceevents LIMIT 57;\nINSERT INTO sourcing SELECT professional_development_programs, account_balance FROM claims_documents WHERE professional_development_programs > 238;\nSQL\n", "labels": {"reads": [{"table": "danceevents", "columns": ["daily_visitors", "address"]}, {"table": "claims_documents", "columns": ["professional_development_programs", "account_balance"]}], "writes": [{"table": "sourcing", "columns": ["professional_development_programs", "account_balance"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cropid > 163).all()\n# src table: genderdistribution\nengine.execute(\"INSERT INTO dw_users_full SELECT * FROM genderdistribution\")\n", "labels": {"reads": [{"table": "genderdistribution", "columns": null}], "writes": [{"table": "dw_users_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO textileworkers SELECT virtual_tour_views, investorgender, assets FROM spacecraft WHERE virtual_tour_views > 450\"\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["virtual_tour_views", "investorgender", "assets"]}], "writes": [{"table": "textileworkers", "columns": ["virtual_tour_views", "investorgender", "assets"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table waste_generation_metrics --target-dir /tmp/land\n", "labels": {"reads": [{"table": "waste_generation_metrics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_depths\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.campaigns_df\")\n", "labels": {"reads": [{"table": "ocean_depths", "columns": null}], "writes": [{"table": "stg.campaigns_df", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shariah_compliant_products depends on militaryequipmentsales\ndbt run -s shariah_compliant_products --vars 'source: militaryequipmentsales'\n", "labels": {"reads": [{"table": "militaryequipmentsales", "columns": null}], "writes": [{"table": "shariah_compliant_products", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM teaches\", conn)\ndf.to_sql(\"hydro_plants\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "teaches", "columns": null}], "writes": [{"table": "hydro_plants", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO streams SELECT workouttype, daily_distance, invoice_id, potency FROM nasa_mars_program WHERE workouttype > 484\")\n", "labels": {"reads": [{"table": "nasa_mars_program", "columns": ["workouttype", "daily_distance", "invoice_id", "potency"]}], "writes": [{"table": "streams", "columns": ["workouttype", "daily_distance", "invoice_id", "potency"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table chemical --target-dir /tmp/land\n", "labels": {"reads": [{"table": "chemical", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"circular_supply_chain_products\")\nsave_to_target(df, \"trainers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "circular_supply_chain_products", "columns": null}], "writes": [{"table": "trainers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_warehouses\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"cuisine\")\n", "labels": {"reads": [{"table": "sustainable_warehouses", "columns": null}], "writes": [{"table": "cuisine", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shipping_agent_name, enrollment_date FROM site LIMIT 425\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "site", "columns": ["shipping_agent_name", "enrollment_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"visitor_exhibition\").toPandas()\ndf[[\"intervention_type\", \"line_id\"]].to_sql(\"order_items\", engine, index=False)\n", "labels": {"reads": [{"table": "visitor_exhibition", "columns": null}], "writes": [{"table": "order_items", "columns": ["intervention_type", "line_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"new_schedules\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"parks\")\n", "labels": {"reads": [{"table": "new_schedules", "columns": null}], "writes": [{"table": "parks", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT averagespeed, people_id FROM australia_offset_programs\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"cases\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "australia_offset_programs", "columns": ["averagespeed", "people_id"]}], "writes": [{"table": "cases", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.risk_score_hourly SELECT inspection_id, number_of_platforms, settlement_amount FROM music_events WHERE inspection_id > 435\"], check=True)\n", "labels": {"reads": [{"table": "music_events", "columns": ["inspection_id", "number_of_platforms", "settlement_amount"]}], "writes": [{"table": "stg.risk_score_hourly", "columns": ["inspection_id", "number_of_platforms", "settlement_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO laptimes SELECT accelerator_id, equipment_id FROM associatedheritages WHERE accelerator_id > 254\")\n", "labels": {"reads": [{"table": "associatedheritages", "columns": ["accelerator_id", "equipment_id"]}], "writes": [{"table": "laptimes", "columns": ["accelerator_id", "equipment_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table underwater_cables --columns review_score,building_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "underwater_cables", "columns": ["review_score", "building_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO organicproducts (reason, date_of_notes) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "organicproducts", "columns": ["reason", "date_of_notes"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT squadron, num_of_staff FROM watertreatmentplants LIMIT 179\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO voting_data SELECT stop, astronaut_name, meter_300 FROM farms WHERE stop > 267\")\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["squadron", "num_of_staff"]}, {"table": "farms", "columns": ["stop", "astronaut_name", "meter_300"]}], "writes": [{"table": "voting_data", "columns": ["stop", "astronaut_name", "meter_300"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO battery_storage SELECT aid, license_type FROM threat_intelligence WHERE aid > 84\")\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["aid", "license_type"]}], "writes": [{"table": "battery_storage", "columns": ["aid", "license_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.publication_year > 162).all()\n# src table: ads.ads_exposure_daily\nengine.execute(\"INSERT INTO financialwellbeing SELECT * FROM ads.ads_exposure_daily\")\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO purchase SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO submersible_dives SELECT hometeam, mouse_id FROM department_store_chain WHERE hometeam > 139\"\n", "labels": {"reads": [{"table": "department_store_chain", "columns": ["hometeam", "mouse_id"]}], "writes": [{"table": "submersible_dives", "columns": ["hometeam", "mouse_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO waste_types SELECT mean_humidity, stu_phone, mission_date, project_category FROM dw_vendors_di WHERE mean_humidity > 374\"\n", "labels": {"reads": [{"table": "dw_vendors_di", "columns": ["mean_humidity", "stu_phone", "mission_date", "project_category"]}], "writes": [{"table": "waste_types", "columns": ["mean_humidity", "stu_phone", "mission_date", "project_category"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacecraft\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"iot_sensors\")\n", "labels": {"reads": [{"table": "spacecraft", "columns": null}], "writes": [{"table": "iot_sensors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO military_personnel SELECT a.specialty, b.eventid FROM safety_testing a JOIN therapists b ON a.avg_usage = b.avg_usage\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "safety_testing", "columns": null}, {"table": "therapists", "columns": null}], "writes": [{"table": "military_personnel", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model organisations depends on artists_valuation\ndbt build --models organisations --vars 'source: artists_valuation'\n", "labels": {"reads": [{"table": "artists_valuation", "columns": null}], "writes": [{"table": "organisations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO venture SELECT virtual_tour_views, line_name, working_year_starts, arrival_time FROM drought_data WHERE virtual_tour_views > 76\"\n", "labels": {"reads": [{"table": "drought_data", "columns": ["virtual_tour_views", "line_name", "working_year_starts", "arrival_time"]}], "writes": [{"table": "venture", "columns": ["virtual_tour_views", "line_name", "working_year_starts", "arrival_time"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wastewatertreatment\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"electoral_register\")\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": null}], "writes": [{"table": "electoral_register", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"criminal_justice_reform_initiatives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"climate_finance_asia\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"regional_railways\")\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": null}], "writes": [{"table": "regional_railways", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO ca_menu_items SELECT a.wifi, b.premise_details FROM tv_shows a JOIN calibration_data2 b ON a.framework = b.framework\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tv_shows", "columns": null}, {"table": "calibration_data2", "columns": null}], "writes": [{"table": "ca_menu_items", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"volunteer_registration\")\nupsert_to_warehouse(df, \"mart.mart_products_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": [{"table": "mart.mart_products_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_populations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "marine_life_populations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table socially_responsible_loans --target-dir /tmp/land\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO artsandcrafts SELECT community_size, goal_id, nation FROM people_addresses WHERE community_size > 286\"\n", "labels": {"reads": [{"table": "people_addresses", "columns": ["community_size", "goal_id", "nation"]}], "writes": [{"table": "artsandcrafts", "columns": ["community_size", "goal_id", "nation"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO government_funding SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO arrivals SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO euroavev SELECT chemical_type, highscore, mean_temperature_f, ticket_subject FROM train_maintenance WHERE chemical_type > 346\")\n", "labels": {"reads": [{"table": "train_maintenance", "columns": ["chemical_type", "highscore", "mean_temperature_f", "ticket_subject"]}], "writes": [{"table": "euroavev", "columns": ["chemical_type", "highscore", "mean_temperature_f", "ticket_subject"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO student_program_mapping SELECT preference_rating, donationamount, apt_type_code, start FROM water_usage WHERE preference_rating > 383\"\n", "labels": {"reads": [{"table": "water_usage", "columns": ["preference_rating", "donationamount", "apt_type_code", "start"]}], "writes": [{"table": "student_program_mapping", "columns": ["preference_rating", "donationamount", "apt_type_code", "start"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM drugs\"\n", "labels": {"reads": [{"table": "drugs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT method_name, fan_age FROM smart_city_projects LIMIT 136\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO labor_cost SELECT total_shipped, assessment_score FROM participants WHERE total_shipped > 204\")\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": ["method_name", "fan_age"]}, {"table": "participants", "columns": ["total_shipped", "assessment_score"]}], "writes": [{"table": "labor_cost", "columns": ["total_shipped", "assessment_score"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO retailers SELECT outcome_type, jul, clublocation, publish_date FROM department_publications WHERE outcome_type > 23\"], check=True)\n", "labels": {"reads": [{"table": "department_publications", "columns": ["outcome_type", "jul", "clublocation", "publish_date"]}], "writes": [{"table": "retailers", "columns": ["outcome_type", "jul", "clublocation", "publish_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO atlantic_plate (founder_gender, rig_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "atlantic_plate", "columns": ["founder_gender", "rig_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainable_materials SELECT founded, emp_id, transit_passengers, num_songs FROM sectors WHERE founded > 379\"\n", "labels": {"reads": [{"table": "sectors", "columns": ["founded", "emp_id", "transit_passengers", "num_songs"]}], "writes": [{"table": "sustainable_materials", "columns": ["founded", "emp_id", "transit_passengers", "num_songs"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO language SELECT contributorname, courses, last_service FROM platformi WHERE contributorname > 299;\nEOF\n", "labels": {"reads": [{"table": "platformi", "columns": ["contributorname", "courses", "last_service"]}], "writes": [{"table": "language", "columns": ["contributorname", "courses", "last_service"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO space_debris SELECT a.issue_date, b.date_of_ceremony FROM ods_shipments_df a JOIN platformstats b ON a.production_cost = b.production_cost\"\n", "labels": {"reads": [{"table": "ods_shipments_df", "columns": null}, {"table": "platformstats", "columns": null}], "writes": [{"table": "space_debris", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO regional_railways SELECT fare, hometeam, data_usage, month FROM class WHERE fare > 123\"], check=True)\n", "labels": {"reads": [{"table": "class", "columns": ["fare", "hometeam", "data_usage", "month"]}], "writes": [{"table": "regional_railways", "columns": ["fare", "hometeam", "data_usage", "month"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO airportdata SELECT a.crs_description, b.worker FROM contract_timeline a JOIN waste_production b ON a.awardid = b.awardid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "contract_timeline", "columns": null}, {"table": "waste_production", "columns": null}], "writes": [{"table": "airportdata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT therapist_id, healthequitymetricscore FROM ads_coupon_use_full\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"inclusivehousingpolicies\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads_coupon_use_full", "columns": ["therapist_id", "healthequitymetricscore"]}], "writes": [{"table": "inclusivehousingpolicies", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO assignedto (equipment_name, equipment_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "assignedto", "columns": ["equipment_name", "equipment_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.home_team_points > 64).all()\n# src table: audience_demographics\nengine.execute(\"INSERT INTO artistsdemographics SELECT * FROM audience_demographics\")\n", "labels": {"reads": [{"table": "audience_demographics", "columns": null}], "writes": [{"table": "artistsdemographics", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT billing, water_temp FROM trust LIMIT 265\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO electric_vehicle_stats SELECT lat, user_name, is_operational FROM fabricdata WHERE lat > 94\")\n", "labels": {"reads": [{"table": "trust", "columns": ["billing", "water_temp"]}, {"table": "fabricdata", "columns": ["lat", "user_name", "is_operational"]}], "writes": [{"table": "electric_vehicle_stats", "columns": ["lat", "user_name", "is_operational"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"space_agencies_2\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "space_agencies_2", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mining_companies\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"creativeais\")\n", "labels": {"reads": [{"table": "mining_companies", "columns": null}], "writes": [{"table": "creativeais", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"species_observations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "species_observations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"smartcityprojects\")\nsink_to_target(df, \"regional_railways\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "smartcityprojects", "columns": null}], "writes": [{"table": "regional_railways", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"workforcediversity\")\ndump_to_store(df, \"bi.bi_payments\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "workforcediversity", "columns": null}], "writes": [{"table": "bi.bi_payments", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dws.exposure_df SELECT hours_developed, heritage_site_id, friend FROM bi.users_full WHERE hours_developed > 210;\nEOF\n", "labels": {"reads": [{"table": "bi.users_full", "columns": ["hours_developed", "heritage_site_id", "friend"]}], "writes": [{"table": "dws.exposure_df", "columns": ["hours_developed", "heritage_site_id", "friend"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO manufacturermaterials SELECT college_location, researcher_name FROM submersible_dives WHERE college_location > 145\"\n", "labels": {"reads": [{"table": "submersible_dives", "columns": ["college_location", "researcher_name"]}], "writes": [{"table": "manufacturermaterials", "columns": ["college_location", "researcher_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT defendant_id, dept_name FROM invoices LIMIT 167\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ytterbiumproduction SELECT log_entry_description, purchaseid FROM ref_product_categories WHERE log_entry_description > 123\")\n", "labels": {"reads": [{"table": "invoices", "columns": ["defendant_id", "dept_name"]}, {"table": "ref_product_categories", "columns": ["log_entry_description", "purchaseid"]}], "writes": [{"table": "ytterbiumproduction", "columns": ["log_entry_description", "purchaseid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO basketball_teams SELECT province_id, employmentdate, unitsperweek FROM fairtradecertifications WHERE province_id > 336\"], check=True)\n", "labels": {"reads": [{"table": "fairtradecertifications", "columns": ["province_id", "employmentdate", "unitsperweek"]}], "writes": [{"table": "basketball_teams", "columns": ["province_id", "employmentdate", "unitsperweek"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table ods.shipments_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.shipments_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO building_permits SELECT court_id, rural_area FROM chemical_processes WHERE court_id > 164\"\n", "labels": {"reads": [{"table": "chemical_processes", "columns": ["court_id", "rural_area"]}], "writes": [{"table": "building_permits", "columns": ["court_id", "rural_area"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bus_routes SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.stg_clicks_delta SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO union_members SELECT trainingname, effort, tree_id, industry_4_0 FROM volunteer_events WHERE trainingname > 176\"\n", "labels": {"reads": [{"table": "volunteer_events", "columns": ["trainingname", "effort", "tree_id", "industry_4_0"]}], "writes": [{"table": "union_members", "columns": ["trainingname", "effort", "tree_id", "industry_4_0"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"miningoperations\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "miningoperations", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table public_schools --columns stadium_id,fund_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "public_schools", "columns": ["stadium_id", "fund_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT union_members, is_sustainable FROM safety_records LIMIT 47\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO bi.bi_exposure_hourly SELECT amount_used, mealname, vulnerability, org_name FROM renewableenergy WHERE amount_used > 349\")\n", "labels": {"reads": [{"table": "safety_records", "columns": ["union_members", "is_sustainable"]}, {"table": "renewableenergy", "columns": ["amount_used", "mealname", "vulnerability", "org_name"]}], "writes": [{"table": "bi.bi_exposure_hourly", "columns": ["amount_used", "mealname", "vulnerability", "org_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"veterans\").toPandas()\ndf[[\"permit_date\", \"device\"]].to_sql(\"southchinasea.wells\", engine, index=False)\n", "labels": {"reads": [{"table": "veterans", "columns": null}], "writes": [{"table": "southchinasea.wells", "columns": ["permit_date", "device"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO bank_info SELECT operationid, review_date, donor_state, coownerid FROM fabricdata WHERE operationid > 164\")\n", "labels": {"reads": [{"table": "fabricdata", "columns": ["operationid", "review_date", "donor_state", "coownerid"]}], "writes": [{"table": "bank_info", "columns": ["operationid", "review_date", "donor_state", "coownerid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO plants SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"block\")\nsink_to_store(df, \"stg.risk_score_hourly\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "block", "columns": null}], "writes": [{"table": "stg.risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM opioid_overdoses\"\n", "labels": {"reads": [{"table": "opioid_overdoses", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT decor, assigned_to_staff_id FROM visualartprograms LIMIT 243;\nINSERT INTO viewership SELECT signupdate, payment_type_code FROM platformh WHERE signupdate > 4;\nSQL\n", "labels": {"reads": [{"table": "visualartprograms", "columns": ["decor", "assigned_to_staff_id"]}, {"table": "platformh", "columns": ["signupdate", "payment_type_code"]}], "writes": [{"table": "viewership", "columns": ["signupdate", "payment_type_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO us_platforms SELECT 1\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT visitors, workouttype FROM tech_workers_union\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"broadband_revenue\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "tech_workers_union", "columns": ["visitors", "workouttype"]}], "writes": [{"table": "broadband_revenue", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO brand_info SELECT worker_name, chip_model, fleet_name FROM branch WHERE worker_name > 184\"], check=True)\n", "labels": {"reads": [{"table": "branch", "columns": ["worker_name", "chip_model", "fleet_name"]}], "writes": [{"table": "brand_info", "columns": ["worker_name", "chip_model", "fleet_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO renewable_energy_projects SELECT eco_friendly, meter_200 FROM traditionalarts WHERE eco_friendly > 129;\nEOF\n", "labels": {"reads": [{"table": "traditionalarts", "columns": ["eco_friendly", "meter_200"]}], "writes": [{"table": "renewable_energy_projects", "columns": ["eco_friendly", "meter_200"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO navalvessels SELECT court_id, participant_name, rig_id, farmname FROM manufacturer WHERE court_id > 7\"\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["court_id", "participant_name", "rig_id", "farmname"]}], "writes": [{"table": "navalvessels", "columns": ["court_id", "participant_name", "rig_id", "farmname"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model government_transparency depends on bank\ndbt build --models government_transparency --vars '{\"source_table\":\"bank\"}'\n", "labels": {"reads": [{"table": "bank", "columns": null}], "writes": [{"table": "government_transparency", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT household_size, total_donation_amount FROM recycling_rates_state LIMIT 223\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO wedding SELECT bridgeid, metric, money_requested, shop_id FROM drought_impact WHERE bridgeid > 217\")\n", "labels": {"reads": [{"table": "recycling_rates_state", "columns": ["household_size", "total_donation_amount"]}, {"table": "drought_impact", "columns": ["bridgeid", "metric", "money_requested", "shop_id"]}], "writes": [{"table": "wedding", "columns": ["bridgeid", "metric", "money_requested", "shop_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO menu SELECT end_date, threat_type FROM caribbean_tourists WHERE end_date > 303\"\n", "labels": {"reads": [{"table": "caribbean_tourists", "columns": ["end_date", "threat_type"]}], "writes": [{"table": "menu", "columns": ["end_date", "threat_type"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ranking, disability_type FROM ref_incident_type\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"county_public_safety\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ref_incident_type", "columns": ["ranking", "disability_type"]}], "writes": [{"table": "county_public_safety", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO dw.dw_sessions_full SELECT a.trial_name, b.festival_id FROM dws.dws_risk_score_daily a JOIN service_budget b ON a.propertyid = b.propertyid\"\n", "labels": {"reads": [{"table": "dws.dws_risk_score_daily", "columns": null}, {"table": "service_budget", "columns": null}], "writes": [{"table": "dw.dw_sessions_full", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"medicine_enzyme_interaction\")\nexport_to_warehouse(df, \"policyanalysis\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "medicine_enzyme_interaction", "columns": null}], "writes": [{"table": "policyanalysis", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads.payments_di SELECT pname, donorname, closure_authorised_by_staff_id, mine_location FROM section WHERE pname > 129;\nEOF\n", "labels": {"reads": [{"table": "section", "columns": ["pname", "donorname", "closure_authorised_by_staff_id", "mine_location"]}], "writes": [{"table": "ads.payments_di", "columns": ["pname", "donorname", "closure_authorised_by_staff_id", "mine_location"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table school_details --target-dir /tmp/land\n", "labels": {"reads": [{"table": "school_details", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO drug_approvals SELECT working_year_starts, field, center_id FROM safetyorgs WHERE working_year_starts > 259\"], check=True)\n", "labels": {"reads": [{"table": "safetyorgs", "columns": ["working_year_starts", "field", "center_id"]}], "writes": [{"table": "drug_approvals", "columns": ["working_year_starts", "field", "center_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecurityincidents\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"exam_results\")\n", "labels": {"reads": [{"table": "cybersecurityincidents", "columns": null}], "writes": [{"table": "exam_results", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO commercialbuildings (individual_name, contractorname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "commercialbuildings", "columns": ["individual_name", "contractorname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO navalvessels SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"thefts\").toPandas()\ndf[[\"access_count\", \"co2_offset_amount\"]].to_sql(\"user_profiles\", engine, index=False)\n", "labels": {"reads": [{"table": "thefts", "columns": null}], "writes": [{"table": "user_profiles", "columns": ["access_count", "co2_offset_amount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads.ads_payments_delta SELECT 1\"\nlogger.info(msg)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"latam_schema.education_budget\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"country_renewable_energy\")\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": null}], "writes": [{"table": "country_renewable_energy", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.num_cases > 133).all()\n# src table: bi.bi_payments_full\nengine.execute(\"INSERT INTO participants SELECT * FROM bi.bi_payments_full\")\n", "labels": {"reads": [{"table": "bi.bi_payments_full", "columns": null}], "writes": [{"table": "participants", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM recyclingcenters\"\n", "labels": {"reads": [{"table": "recyclingcenters", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM safety_testing\"\n", "labels": {"reads": [{"table": "safety_testing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bioprocess_engineering --columns rebounds,mean_visibility_miles --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bioprocess_engineering", "columns": ["rebounds", "mean_visibility_miles"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"excavations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"shipments\")\n", "labels": {"reads": [{"table": "excavations", "columns": null}], "writes": [{"table": "shipments", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT filingdate, facultyid FROM recyclingrates LIMIT 117\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO waste_management_projects SELECT factory_name, grant_name FROM circular_economy_companies WHERE factory_name > 480\")\n", "labels": {"reads": [{"table": "recyclingrates", "columns": ["filingdate", "facultyid"]}, {"table": "circular_economy_companies", "columns": ["factory_name", "grant_name"]}], "writes": [{"table": "waste_management_projects", "columns": ["factory_name", "grant_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table teachers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "teachers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"support_groups\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dorm_amenity\")\n", "labels": {"reads": [{"table": "support_groups", "columns": null}], "writes": [{"table": "dorm_amenity", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climate_finance_organizations SELECT num_hotels, potency, investor_details FROM suburbs WHERE num_hotels > 382\"\n", "labels": {"reads": [{"table": "suburbs", "columns": ["num_hotels", "potency", "investor_details"]}], "writes": [{"table": "climate_finance_organizations", "columns": ["num_hotels", "potency", "investor_details"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mobile_plans (bill_id, energy_consumption) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mobile_plans", "columns": ["bill_id", "energy_consumption"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT founders_lgbtq, book_id FROM donations LIMIT 97\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "donations", "columns": ["founders_lgbtq", "book_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT strain, ship_name FROM onlineengagement LIMIT 415;\nINSERT INTO high_risk SELECT vehicleid, production_quantity FROM submarine_canyons WHERE vehicleid > 18;\nSQL\n", "labels": {"reads": [{"table": "onlineengagement", "columns": ["strain", "ship_name"]}, {"table": "submarine_canyons", "columns": ["vehicleid", "production_quantity"]}], "writes": [{"table": "high_risk", "columns": ["vehicleid", "production_quantity"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ingredient (file_size, date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ingredient", "columns": ["file_size", "date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table customer_transactions --columns vehicle_name,business_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "customer_transactions", "columns": ["vehicle_name", "business_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO waste_generation_metrics SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO workplaces SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model skincareproducts depends on winter_olympics\ndbt build --select skincareproducts --vars '{\"src\":\"winter_olympics\"}'\n", "labels": {"reads": [{"table": "winter_olympics", "columns": null}], "writes": [{"table": "skincareproducts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_registration\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"initiatives\")\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": null}], "writes": [{"table": "initiatives", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO office_locations SELECT trial_status, ram_mib, cityname, trend_id FROM factories WHERE trial_status > 34\"], check=True)\n", "labels": {"reads": [{"table": "factories", "columns": ["trial_status", "ram_mib", "cityname", "trend_id"]}], "writes": [{"table": "office_locations", "columns": ["trial_status", "ram_mib", "cityname", "trend_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM property_community\", conn)\ndf.to_sql(\"seeds\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "property_community", "columns": null}], "writes": [{"table": "seeds", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table industrial_customers --columns professionalid,staff_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "industrial_customers", "columns": ["professionalid", "staff_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO prescribes SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO bi.bi_campaigns_daily SELECT a.operationid, b.attendance FROM art a JOIN stg.stg_shipments_hourly b ON a.enable_location_tracking = b.enable_location_tracking\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "art", "columns": null}, {"table": "stg.stg_shipments_hourly", "columns": null}], "writes": [{"table": "bi.bi_campaigns_daily", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM user_activity\", conn)\ndf.to_sql(\"mediators\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "user_activity", "columns": null}], "writes": [{"table": "mediators", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.guest_id > 57).all()\n# src table: projecttimelinebybudget\nengine.execute(\"INSERT INTO browser SELECT * FROM projecttimelinebybudget\")\n", "labels": {"reads": [{"table": "projecttimelinebybudget", "columns": null}], "writes": [{"table": "browser", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_acidity\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO heritage_sites_3 (skill_description, participant_type_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "heritage_sites_3", "columns": ["skill_description", "participant_type_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"economic_diversification_argentina\")\nsrc.write.insertInto(\"ods_risk_score_delta\", overwrite=True)\n", "labels": {"reads": [{"table": "economic_diversification_argentina", "columns": null}], "writes": [{"table": "ods_risk_score_delta", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"student_program_mapping\")\nupsert_to_sink(df, \"skills\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": null}], "writes": [{"table": "skills", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO drills SELECT * FROM legacy\ncur.execute(\"SELECT lawyer_name, artwork_id FROM communitydevelopment LIMIT 249\")\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": ["lawyer_name", "artwork_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO phone_market SELECT composer, produceid, building_description FROM salary WHERE composer > 337\"\n", "labels": {"reads": [{"table": "salary", "columns": ["composer", "produceid", "building_description"]}], "writes": [{"table": "phone_market", "columns": ["composer", "produceid", "building_description"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO canals SELECT a.player_api_id, b.school_id FROM customer a JOIN parking_fines b ON a.loan_id = b.loan_id\"\n", "labels": {"reads": [{"table": "customer", "columns": null}, {"table": "parking_fines", "columns": null}], "writes": [{"table": "canals", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT algorithm_name, round_type FROM programs LIMIT 280\")\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO indigenous_food_systems SELECT orderid, fairness_score FROM ods.campaigns_di WHERE orderid > 35\")\n", "labels": {"reads": [{"table": "programs", "columns": ["algorithm_name", "round_type"]}, {"table": "ods.campaigns_di", "columns": ["orderid", "fairness_score"]}], "writes": [{"table": "indigenous_food_systems", "columns": ["orderid", "fairness_score"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM forest\"\n", "labels": {"reads": [{"table": "forest", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT year_deforested, major FROM district_schools LIMIT 371;\nINSERT INTO humanitarian_aid SELECT accreditation_level, incident_category FROM rural_development.agriculture_projects WHERE accreditation_level > 376;\nSQL\n", "labels": {"reads": [{"table": "district_schools", "columns": ["year_deforested", "major"]}, {"table": "rural_development.agriculture_projects", "columns": ["accreditation_level", "incident_category"]}], "writes": [{"table": "humanitarian_aid", "columns": ["accreditation_level", "incident_category"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table licenses --columns diagnosis,farmland_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "licenses", "columns": ["diagnosis", "farmland_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM mountain\"\n", "labels": {"reads": [{"table": "mountain", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO producesupplier SELECT a.build_year, b.trainingdate FROM brandrevenue a JOIN bi.refunds_daily b ON a.festival_name = b.festival_name\"\n", "labels": {"reads": [{"table": "brandrevenue", "columns": null}, {"table": "bi.refunds_daily", "columns": null}], "writes": [{"table": "producesupplier", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.dwd_products --columns deaths,business_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_products", "columns": ["deaths", "business_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bioprocess.engineering_projects (vendor_state, platform_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bioprocess.engineering_projects", "columns": ["vendor_state", "platform_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO crime_reports SELECT a.inclusivehousing, b.shipment_id FROM city_properties a JOIN research_grants b ON a.customerid = b.customerid\"\n", "labels": {"reads": [{"table": "city_properties", "columns": null}, {"table": "research_grants", "columns": null}], "writes": [{"table": "crime_reports", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.permit_id > 32).all()\n# src table: ticket_sales\nengine.execute(\"INSERT INTO passenger_trips SELECT * FROM ticket_sales\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": null}], "writes": [{"table": "passenger_trips", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stg.campaigns_df SELECT * FROM legacy\ncur.execute(\"SELECT clublocation, number_thousands FROM overwatch_scores LIMIT 305\")\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": ["clublocation", "number_thousands"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO regular_order_products SELECT partnership_id, energy_efficiency_savings, job_title, lesson_id FROM dw.dw_coupon_use_daily WHERE partnership_id > 329;\nEOF\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": ["partnership_id", "energy_efficiency_savings", "job_title", "lesson_id"]}], "writes": [{"table": "regular_order_products", "columns": ["partnership_id", "energy_efficiency_savings", "job_title", "lesson_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO restaurant SELECT 1\"\nlogger.info(msg)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO territory.human_rights_data (complaint_date, archaeologistid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "territory.human_rights_data", "columns": ["complaint_date", "archaeologistid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"state_usage\")\nsrc.write.insertInto(\"material_production\", overwrite=True)\n", "labels": {"reads": [{"table": "state_usage", "columns": null}], "writes": [{"table": "material_production", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO renewable_power SELECT * FROM legacy\ncur.execute(\"SELECT nominee, operation_name FROM fossil_fuel_vehicles LIMIT 146\")\n", "labels": {"reads": [{"table": "fossil_fuel_vehicles", "columns": ["nominee", "operation_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO donation SELECT operationid, products_this_year FROM food_justice WHERE operationid > 485\"\n", "labels": {"reads": [{"table": "food_justice", "columns": ["operationid", "products_this_year"]}], "writes": [{"table": "donation", "columns": ["operationid", "products_this_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table prepaid_mobile --target-dir /tmp/land\n", "labels": {"reads": [{"table": "prepaid_mobile", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_sustainable_chains\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM resilience_infrastructure\"\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO wells SELECT subject_id, award, threat FROM shariah_compliant_loans WHERE subject_id > 224\"\n", "labels": {"reads": [{"table": "shariah_compliant_loans", "columns": ["subject_id", "award", "threat"]}], "writes": [{"table": "wells", "columns": ["subject_id", "award", "threat"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"broadband_providers\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "broadband_providers", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT wrestler_id, budget_in_billions FROM hospitallocations\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"manufacturermaterials\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "hospitallocations", "columns": ["wrestler_id", "budget_in_billions"]}], "writes": [{"table": "manufacturermaterials", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"city.community_policing\")\npush_to_output(df, \"art_exhibit_attendance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "city.community_policing", "columns": null}], "writes": [{"table": "art_exhibit_attendance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO device_accessibility SELECT * FROM legacy\ncur.execute(\"SELECT built_year, cuisine_id FROM urban_transportation LIMIT 383\")\n", "labels": {"reads": [{"table": "urban_transportation", "columns": ["built_year", "cuisine_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO flights SELECT wage, eventname, price_in_dollars FROM ads.ads_member_point_daily WHERE wage > 350\")\n", "labels": {"reads": [{"table": "ads.ads_member_point_daily", "columns": ["wage", "eventname", "price_in_dollars"]}], "writes": [{"table": "flights", "columns": ["wage", "eventname", "price_in_dollars"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"bi.bi_orders_hourly\")\ndump_to_sink(df, \"fields_production\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": null}], "writes": [{"table": "fields_production", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO farmers_india SELECT contract_start_date, inspectionid, galleryname, workoutdate FROM green_building_materials WHERE contract_start_date > 478\"\n", "labels": {"reads": [{"table": "green_building_materials", "columns": ["contract_start_date", "inspectionid", "galleryname", "workoutdate"]}], "writes": [{"table": "farmers_india", "columns": ["contract_start_date", "inspectionid", "galleryname", "workoutdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT grade, partitionid FROM precision_farming_imagery\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"boston_emergency_response\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "precision_farming_imagery", "columns": ["grade", "partitionid"]}], "writes": [{"table": "boston_emergency_response", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO marine_life_research SELECT trade, component_type, event_id FROM wastewater_treatment_plants WHERE trade > 140\"\n", "labels": {"reads": [{"table": "wastewater_treatment_plants", "columns": ["trade", "component_type", "event_id"]}], "writes": [{"table": "marine_life_research", "columns": ["trade", "component_type", "event_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dws_cart_item --columns rural_area,facultyid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dws_cart_item", "columns": ["rural_area", "facultyid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads_cart_item_hourly SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table audience_demographics --target-dir /tmp/land\n", "labels": {"reads": [{"table": "audience_demographics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO customer_master_index SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO extraction_methods (patient_age, order_quantity) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "extraction_methods", "columns": ["patient_age", "order_quantity"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO stg.stg_users SELECT datetime_detention_start, attendanceid FROM public_schools WHERE datetime_detention_start > 166\"\n", "labels": {"reads": [{"table": "public_schools", "columns": ["datetime_detention_start", "attendanceid"]}], "writes": [{"table": "stg.stg_users", "columns": ["datetime_detention_start", "attendanceid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO web_client_accelerator SELECT forest_type, founders FROM haircare_sales WHERE forest_type > 21\"], check=True)\n", "labels": {"reads": [{"table": "haircare_sales", "columns": ["forest_type", "founders"]}], "writes": [{"table": "web_client_accelerator", "columns": ["forest_type", "founders"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO savings SELECT contract_address, extraction_state, author_or_editor, country_name FROM ads.ads_exposure_di WHERE contract_address > 458\"\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["contract_address", "extraction_state", "author_or_editor", "country_name"]}], "writes": [{"table": "savings", "columns": ["contract_address", "extraction_state", "author_or_editor", "country_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"brands\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"check_ins\")\n", "labels": {"reads": [{"table": "brands", "columns": null}], "writes": [{"table": "check_ins", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO threats SELECT vessel, uk_vat_number FROM stg.stg_campaigns WHERE vessel > 451\"\n", "labels": {"reads": [{"table": "stg.stg_campaigns", "columns": ["vessel", "uk_vat_number"]}], "writes": [{"table": "threats", "columns": ["vessel", "uk_vat_number"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workshops\").toPandas()\ndf[[\"transaction_value\", \"maintenanceid\"]].to_sql(\"solar_farms\", engine, index=False)\n", "labels": {"reads": [{"table": "workshops", "columns": null}], "writes": [{"table": "solar_farms", "columns": ["transaction_value", "maintenanceid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table oceanography --target-dir /tmp/land\n", "labels": {"reads": [{"table": "oceanography", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO spaceexploration SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"auctions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"green_buildings_us\")\n", "labels": {"reads": [{"table": "auctions", "columns": null}], "writes": [{"table": "green_buildings_us", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"show\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "show", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT unionname, characteristic_type_code FROM landfillcapacitybycountry LIMIT 11;\nINSERT INTO residents_services SELECT app_id, route_name, secretary_vote FROM ai_safety_incidents WHERE app_id > 33;\nSQL\n", "labels": {"reads": [{"table": "landfillcapacitybycountry", "columns": ["unionname", "characteristic_type_code"]}, {"table": "ai_safety_incidents", "columns": ["app_id", "route_name", "secretary_vote"]}], "writes": [{"table": "residents_services", "columns": ["app_id", "route_name", "secretary_vote"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO circuits SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.member_point_df SELECT attendeeid, batting_average, playdate FROM player_sessions WHERE attendeeid > 328\"], check=True)\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["attendeeid", "batting_average", "playdate"]}], "writes": [{"table": "mart.member_point_df", "columns": ["attendeeid", "batting_average", "playdate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"runs\")\nsrc.write.insertInto(\"military_expenditure\", overwrite=True)\n", "labels": {"reads": [{"table": "runs", "columns": null}], "writes": [{"table": "military_expenditure", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table rent_arrears --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rent_arrears", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM fare_collection\", conn)\ndf.to_sql(\"lenders\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "fare_collection", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"songs_length\")\nsrc.write.insertInto(\"station_emergencies\", overwrite=True)\n", "labels": {"reads": [{"table": "songs_length", "columns": null}], "writes": [{"table": "station_emergencies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT asset_id, prod_date FROM payments LIMIT 419\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "payments", "columns": ["asset_id", "prod_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"africa_schema.african_mines\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads_vendors_hourly\")\n", "labels": {"reads": [{"table": "africa_schema.african_mines", "columns": null}], "writes": [{"table": "ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_clicks_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws_clicks_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table art_collection --target-dir /tmp/land\n", "labels": {"reads": [{"table": "art_collection", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table restaurants_tx --columns event_type_id,issue_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "restaurants_tx", "columns": ["event_type_id", "issue_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO hotels SELECT rainfall, election_cycle, oil_production_q4_2021 FROM complaints_breakdown WHERE rainfall > 179\"\n", "labels": {"reads": [{"table": "complaints_breakdown", "columns": ["rainfall", "election_cycle", "oil_production_q4_2021"]}], "writes": [{"table": "hotels", "columns": ["rainfall", "election_cycle", "oil_production_q4_2021"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table calibration_data2 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "calibration_data2", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"campaigns\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chemical_composition\")\n", "labels": {"reads": [{"table": "campaigns", "columns": null}], "writes": [{"table": "chemical_composition", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO countryintelligenceops SELECT a.ngo_id, b.permitdate FROM technology_access a JOIN education_union b ON a.fault_description = b.fault_description\"\n", "labels": {"reads": [{"table": "technology_access", "columns": null}, {"table": "education_union", "columns": null}], "writes": [{"table": "countryintelligenceops", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ads.ads_users_hourly SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO supplier_addresses SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO invoice_lines (duration, art) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "invoice_lines", "columns": ["duration", "art"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bi_orders_daily (stat_id, purchase_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bi_orders_daily", "columns": ["stat_id", "purchase_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table certificate --target-dir /tmp/land\n", "labels": {"reads": [{"table": "certificate", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO settlements SELECT a.airport_id, b.area_ha FROM maintenance_requests a JOIN dispensarysales b ON a.dish_type = b.dish_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "maintenance_requests", "columns": null}, {"table": "dispensarysales", "columns": null}], "writes": [{"table": "settlements", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"program_budget\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"space_missions_2\")\n", "labels": {"reads": [{"table": "program_budget", "columns": null}], "writes": [{"table": "space_missions_2", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tickets\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"menu_item\")\n", "labels": {"reads": [{"table": "tickets", "columns": null}], "writes": [{"table": "menu_item", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO smartcityprojects SELECT ingredient, primary_advisor FROM incarcerated WHERE ingredient > 377\"\n", "labels": {"reads": [{"table": "incarcerated", "columns": ["ingredient", "primary_advisor"]}], "writes": [{"table": "smartcityprojects", "columns": ["ingredient", "primary_advisor"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO county_public_safety (quantity_sold, days) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "county_public_safety", "columns": ["quantity_sold", "days"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO recyclers (propertyid, hospitalname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "recyclers", "columns": ["propertyid", "hospitalname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model competition depends on bi.bi_exposure_hourly\ndbt build --models competition --vars 'source: bi.bi_exposure_hourly'\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": null}], "writes": [{"table": "competition", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO culturalpractices SELECT * FROM legacy\ncur.execute(\"SELECT ship_id, velocity FROM reo_production LIMIT 156\")\n", "labels": {"reads": [{"table": "reo_production", "columns": ["ship_id", "velocity"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO space_telescopes SELECT category, color_code, cell_mobile_phone_number, mission_name FROM carbon_prices_3 WHERE category > 27\"\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["category", "color_code", "cell_mobile_phone_number", "mission_name"]}], "writes": [{"table": "space_telescopes", "columns": ["category", "color_code", "cell_mobile_phone_number", "mission_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mobile_usage\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"property_community\")\n", "labels": {"reads": [{"table": "mobile_usage", "columns": null}], "writes": [{"table": "property_community", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO shared_rides_tokyo SELECT menu_item_id, users_engaged, laborproductivity, student_id FROM therapists WHERE menu_item_id > 304\"\n", "labels": {"reads": [{"table": "therapists", "columns": ["menu_item_id", "users_engaged", "laborproductivity", "student_id"]}], "writes": [{"table": "shared_rides_tokyo", "columns": ["menu_item_id", "users_engaged", "laborproductivity", "student_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model marine_life_data depends on concert_revenue\ndbt run --models marine_life_data --vars '{\"src\":\"concert_revenue\"}'\n", "labels": {"reads": [{"table": "concert_revenue", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM russia_nato_diplomacy\"\n", "labels": {"reads": [{"table": "russia_nato_diplomacy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_risk_score_df SELECT a.annual_entry_exit, b.portfolio_id FROM technician a JOIN playergamehistory b ON a.consumption = b.consumption\"\n", "labels": {"reads": [{"table": "technician", "columns": null}, {"table": "playergamehistory", "columns": null}], "writes": [{"table": "bi.bi_risk_score_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model regulatoryframeworksbycountry depends on defense_diplomacy\ndbt run --select regulatoryframeworksbycountry --vars '{\"source_table\":\"defense_diplomacy\"}'\n", "labels": {"reads": [{"table": "defense_diplomacy", "columns": null}], "writes": [{"table": "regulatoryframeworksbycountry", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT time_month, sample_date FROM machinery LIMIT 297;\nINSERT INTO matches SELECT src_apid, production_bopd, billid, business_name FROM mars_spacecraft WHERE src_apid > 80;\nSQL\n", "labels": {"reads": [{"table": "machinery", "columns": ["time_month", "sample_date"]}, {"table": "mars_spacecraft", "columns": ["src_apid", "production_bopd", "billid", "business_name"]}], "writes": [{"table": "matches", "columns": ["src_apid", "production_bopd", "billid", "business_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO entrepreneur SELECT date_incident_end, farm_name FROM engineer_visits WHERE date_incident_end > 73\"\n", "labels": {"reads": [{"table": "engineer_visits", "columns": ["date_incident_end", "farm_name"]}], "writes": [{"table": "entrepreneur", "columns": ["date_incident_end", "farm_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods_risk_score_delta (reader_id, participatedinesports) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods_risk_score_delta", "columns": ["reader_id", "participatedinesports"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO accounts SELECT a.start_time, b.disease FROM ecohousing a JOIN humanitarian_aid b ON a.organization_name = b.organization_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ecohousing", "columns": null}, {"table": "humanitarian_aid", "columns": null}], "writes": [{"table": "accounts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model product_ingredient depends on courtcases\ndbt run --models product_ingredient --vars '{\"src\":\"courtcases\"}'\n", "labels": {"reads": [{"table": "courtcases", "columns": null}], "writes": [{"table": "product_ingredient", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"marine_mammals\")\nexport_to_target(df, \"food_assistance\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "marine_mammals", "columns": null}], "writes": [{"table": "food_assistance", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_inventory_df SELECT a.active_to_date, b.bill_id FROM daily_revenue a JOIN innovation_grants b ON a.project_type = b.project_type\"\n", "labels": {"reads": [{"table": "daily_revenue", "columns": null}, {"table": "innovation_grants", "columns": null}], "writes": [{"table": "ads.ads_inventory_df", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO unionnegotiations SELECT awardid, prod_id, incidentid FROM traditional_arts WHERE awardid > 248\"], check=True)\n", "labels": {"reads": [{"table": "traditional_arts", "columns": ["awardid", "prod_id", "incidentid"]}], "writes": [{"table": "unionnegotiations", "columns": ["awardid", "prod_id", "incidentid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"green_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"storage\")\n", "labels": {"reads": [{"table": "green_projects", "columns": null}], "writes": [{"table": "storage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mediators SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mart_events_di depends on emerging_markets.digital_assets\ndbt run --models mart_events_di --vars 'source: emerging_markets.digital_assets'\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": null}], "writes": [{"table": "mart_events_di", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO zip_codes SELECT animal_name, drug_name FROM catalog_contents WHERE animal_name > 87\"\n", "labels": {"reads": [{"table": "catalog_contents", "columns": ["animal_name", "drug_name"]}], "writes": [{"table": "zip_codes", "columns": ["animal_name", "drug_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.founder_ethnicity > 100).all()\n# src table: student_mental_health\nengine.execute(\"INSERT INTO events SELECT * FROM student_mental_health\")\n", "labels": {"reads": [{"table": "student_mental_health", "columns": null}], "writes": [{"table": "events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table model_fairness --columns successful_cb,wifi --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "model_fairness", "columns": ["successful_cb", "wifi"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.productname > 356).all()\n# src table: deep_sea_expeditions\nengine.execute(\"INSERT INTO stg.stg_coupon_use_di SELECT * FROM deep_sea_expeditions\")\n", "labels": {"reads": [{"table": "deep_sea_expeditions", "columns": null}], "writes": [{"table": "stg.stg_coupon_use_di", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mines\")\nsrc.write.insertInto(\"circular_economy\", overwrite=True)\n", "labels": {"reads": [{"table": "mines", "columns": null}], "writes": [{"table": "circular_economy", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model surveylocations depends on bikerental\ndbt run -s surveylocations --vars 'source: bikerental'\n", "labels": {"reads": [{"table": "bikerental", "columns": null}], "writes": [{"table": "surveylocations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO public.trips_by_day_train SELECT a.num_transactions, b.call_id FROM space_missions a JOIN incident_region b ON a.citation_time = b.citation_time\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "space_missions", "columns": null}, {"table": "incident_region", "columns": null}], "writes": [{"table": "public.trips_by_day_train", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"gymnast\")\nexport_to_warehouse(df, \"renewableenergyprojects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gymnast", "columns": null}], "writes": [{"table": "renewableenergyprojects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ties, cause FROM reservoirs\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"workforcediversity\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "reservoirs", "columns": ["ties", "cause"]}], "writes": [{"table": "workforcediversity", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"brazil_projects\").toPandas()\ndf[[\"eco_certified\", \"continent_id\"]].to_sql(\"contract_negotiations_un\", engine, index=False)\n", "labels": {"reads": [{"table": "brazil_projects", "columns": null}], "writes": [{"table": "contract_negotiations_un", "columns": ["eco_certified", "continent_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sale_amount, contact_staff_id FROM browser LIMIT 65;\nINSERT INTO feedback SELECT event_type_id, severity, county_id, safety_score FROM dws.dws_campaigns_df WHERE event_type_id > 171;\nSQL\n", "labels": {"reads": [{"table": "browser", "columns": ["sale_amount", "contact_staff_id"]}, {"table": "dws.dws_campaigns_df", "columns": ["event_type_id", "severity", "county_id", "safety_score"]}], "writes": [{"table": "feedback", "columns": ["event_type_id", "severity", "county_id", "safety_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT container_id, completion_date FROM accelerator_compatible_browser LIMIT 402\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": ["container_id", "completion_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_spending_3\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artwork\")\n", "labels": {"reads": [{"table": "defense_spending_3", "columns": null}], "writes": [{"table": "artwork", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT eventdate, vulnerability FROM ads.risk_score LIMIT 332;\nINSERT INTO artsandcrafts SELECT retailer_name, active_from_date, inclusive_housing_policy FROM maintenance_requests WHERE retailer_name > 309;\nSQL\n", "labels": {"reads": [{"table": "ads.risk_score", "columns": ["eventdate", "vulnerability"]}, {"table": "maintenance_requests", "columns": ["retailer_name", "active_from_date", "inclusive_housing_policy"]}], "writes": [{"table": "artsandcrafts", "columns": ["retailer_name", "active_from_date", "inclusive_housing_policy"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_sustainable_chains\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dw.users_hourly\")\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": null}], "writes": [{"table": "dw.users_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ads.ads_shipments_delta SELECT player_id, engagement_count, surname FROM useracct WHERE player_id > 50;\nEOF\n", "labels": {"reads": [{"table": "useracct", "columns": ["player_id", "engagement_count", "surname"]}], "writes": [{"table": "ads.ads_shipments_delta", "columns": ["player_id", "engagement_count", "surname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO youth_fan_participation SELECT grant_date, source_system_code FROM workforce_development_programs WHERE grant_date > 463\"], check=True)\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": ["grant_date", "source_system_code"]}], "writes": [{"table": "youth_fan_participation", "columns": ["grant_date", "source_system_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"management\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"customer_month\")\n", "labels": {"reads": [{"table": "management", "columns": null}], "writes": [{"table": "customer_month", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ratings SELECT away_team_points, acc_regular_season FROM ads_exposure_hourly WHERE away_team_points > 436\"], check=True)\n", "labels": {"reads": [{"table": "ads_exposure_hourly", "columns": ["away_team_points", "acc_regular_season"]}], "writes": [{"table": "ratings", "columns": ["away_team_points", "acc_regular_season"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM repair_assignment\", conn)\ndf.to_sql(\"member\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "repair_assignment", "columns": null}], "writes": [{"table": "member", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT event_date, post_category FROM country_sustainable_chains LIMIT 388\")\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO autoshow SELECT num_accessible_tech_centers, park_id FROM development_hours WHERE num_accessible_tech_centers > 430\")\n", "labels": {"reads": [{"table": "country_sustainable_chains", "columns": ["event_date", "post_category"]}, {"table": "development_hours", "columns": ["num_accessible_tech_centers", "park_id"]}], "writes": [{"table": "autoshow", "columns": ["num_accessible_tech_centers", "park_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO mart.clicks SELECT evaluated_for_fairness, album_id, starttime FROM fair_trade_suppliers WHERE evaluated_for_fairness > 248\")\n", "labels": {"reads": [{"table": "fair_trade_suppliers", "columns": ["evaluated_for_fairness", "album_id", "starttime"]}], "writes": [{"table": "mart.clicks", "columns": ["evaluated_for_fairness", "album_id", "starttime"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO equipment_maintenance SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT district, addressid FROM player_f LIMIT 155;\nINSERT INTO roles SELECT player, journal, document_id FROM hospital WHERE player > 196;\nSQL\n", "labels": {"reads": [{"table": "player_f", "columns": ["district", "addressid"]}, {"table": "hospital", "columns": ["player", "journal", "document_id"]}], "writes": [{"table": "roles", "columns": ["player", "journal", "document_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fish_purchases SELECT royal_family_details, amount_claimed, worker_count, hours_spent FROM manager WHERE royal_family_details > 262\"], check=True)\n", "labels": {"reads": [{"table": "manager", "columns": ["royal_family_details", "amount_claimed", "worker_count", "hours_spent"]}], "writes": [{"table": "fish_purchases", "columns": ["royal_family_details", "amount_claimed", "worker_count", "hours_spent"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT class_section, creator FROM stg.stg_products_full LIMIT 101\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "stg.stg_products_full", "columns": ["class_section", "creator"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artpieces\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"renewable_power\")\n", "labels": {"reads": [{"table": "artpieces", "columns": null}], "writes": [{"table": "renewable_power", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_products_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model reporters depends on stg.refunds_daily\ndbt run --select reporters --vars '{\"source_table\":\"stg.refunds_daily\"}'\n", "labels": {"reads": [{"table": "stg.refunds_daily", "columns": null}], "writes": [{"table": "reporters", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO therapy_attendance SELECT a.ota_id, b.contract_name FROM circularsupplychain a JOIN settlements b ON a.innovation = b.innovation\"\n", "labels": {"reads": [{"table": "circularsupplychain", "columns": null}, {"table": "settlements", "columns": null}], "writes": [{"table": "therapy_attendance", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM courtcases\"\n", "labels": {"reads": [{"table": "courtcases", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model stg_products_full depends on support\ndbt run --select stg_products_full --vars '{\"source_table\":\"support\"}'\n", "labels": {"reads": [{"table": "support", "columns": null}], "writes": [{"table": "stg_products_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table circularsupplychain --columns accreditation_type,aircraft_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "circularsupplychain", "columns": ["accreditation_type", "aircraft_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO heritage_sites SELECT siteid, worker_name, u_id, playerid FROM wta_serves WHERE siteid > 478\"\n", "labels": {"reads": [{"table": "wta_serves", "columns": ["siteid", "worker_name", "u_id", "playerid"]}], "writes": [{"table": "heritage_sites", "columns": ["siteid", "worker_name", "u_id", "playerid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ref_product_categories SELECT launch_company, ip_address, sd_id, course_completion FROM dw.dw_member_point_di WHERE launch_company > 333\"\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": ["launch_company", "ip_address", "sd_id", "course_completion"]}], "writes": [{"table": "ref_product_categories", "columns": ["launch_company", "ip_address", "sd_id", "course_completion"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dysprosiumproduction\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"student_course_registrations\")\n", "labels": {"reads": [{"table": "dysprosiumproduction", "columns": null}], "writes": [{"table": "student_course_registrations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"tb_reports\")\nsrc.write.insertInto(\"cultural_heritage\", overwrite=True)\n", "labels": {"reads": [{"table": "tb_reports", "columns": null}], "writes": [{"table": "cultural_heritage", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"drama_workshop_groups\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"athlete_wellbeing\")\n", "labels": {"reads": [{"table": "drama_workshop_groups", "columns": null}], "writes": [{"table": "athlete_wellbeing", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO steps SELECT a.energy_source, b.form_type_code FROM ads.ads_vendors_hourly a JOIN atlantic_plate b ON a.isfirstattendee = b.isfirstattendee\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": null}, {"table": "atlantic_plate", "columns": null}], "writes": [{"table": "steps", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"studentaccommodations\").toPandas()\ndf[[\"game_id\", \"stu_num\"]].to_sql(\"supportprograms\", engine, index=False)\n", "labels": {"reads": [{"table": "studentaccommodations", "columns": null}], "writes": [{"table": "supportprograms", "columns": ["game_id", "stu_num"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT killed, fish_count FROM soccer_goals LIMIT 15\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO manufacturermaterials SELECT airline, archaeologistid, production_volume FROM veteran_stats WHERE airline > 274\")\n", "labels": {"reads": [{"table": "soccer_goals", "columns": ["killed", "fish_count"]}, {"table": "veteran_stats", "columns": ["airline", "archaeologistid", "production_volume"]}], "writes": [{"table": "manufacturermaterials", "columns": ["airline", "archaeologistid", "production_volume"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO crop_yield SELECT home_team_points, currency, contract_date FROM network_infrastructure WHERE home_team_points > 106\"\n", "labels": {"reads": [{"table": "network_infrastructure", "columns": ["home_team_points", "currency", "contract_date"]}], "writes": [{"table": "crop_yield", "columns": ["home_team_points", "currency", "contract_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO community_development_projects (dock_status, degrees) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "community_development_projects", "columns": ["dock_status", "degrees"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO manager_award SELECT line_1, address_content, team_id_loser, meter_100 FROM spacemissions WHERE line_1 > 191\"\n", "labels": {"reads": [{"table": "spacemissions", "columns": ["line_1", "address_content", "team_id_loser", "meter_100"]}], "writes": [{"table": "manager_award", "columns": ["line_1", "address_content", "team_id_loser", "meter_100"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO well_production SELECT low_estimate, num_of_stock, retweets, athlete_name FROM diversity WHERE low_estimate > 401\"\n", "labels": {"reads": [{"table": "diversity", "columns": ["low_estimate", "num_of_stock", "retweets", "athlete_name"]}], "writes": [{"table": "well_production", "columns": ["low_estimate", "num_of_stock", "retweets", "athlete_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO stg.stg_risk_score SELECT healthequitymetricscore, attribute_data_type FROM fans WHERE healthequitymetricscore > 346\"\n", "labels": {"reads": [{"table": "fans", "columns": ["healthequitymetricscore", "attribute_data_type"]}], "writes": [{"table": "stg.stg_risk_score", "columns": ["healthequitymetricscore", "attribute_data_type"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO movie_ratings SELECT circuitid, monthlyactiveusers FROM carbon_prices_3 WHERE circuitid > 327\")\n", "labels": {"reads": [{"table": "carbon_prices_3", "columns": ["circuitid", "monthlyactiveusers"]}], "writes": [{"table": "movie_ratings", "columns": ["circuitid", "monthlyactiveusers"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO volunteer_hours SELECT recruitername, garment_id, labor_practice FROM mart.mart_shipments_hourly WHERE recruitername > 294\"\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["recruitername", "garment_id", "labor_practice"]}], "writes": [{"table": "volunteer_hours", "columns": ["recruitername", "garment_id", "labor_practice"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO renewableprojects SELECT incident_type_description, research_id, exploited, division FROM eu_data_usage WHERE incident_type_description > 449\"], check=True)\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["incident_type_description", "research_id", "exploited", "division"]}], "writes": [{"table": "renewableprojects", "columns": ["incident_type_description", "research_id", "exploited", "division"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"refugees\")\nwrite_to_store(df, \"mart.mart_payments_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "refugees", "columns": null}], "writes": [{"table": "mart.mart_payments_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO energy_consumption SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"maintenancerequests\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"excavations\")\n", "labels": {"reads": [{"table": "maintenancerequests", "columns": null}], "writes": [{"table": "excavations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO train SELECT vessel_id, functional_area_description FROM scan_dates WHERE vessel_id > 208\"\n", "labels": {"reads": [{"table": "scan_dates", "columns": ["vessel_id", "functional_area_description"]}], "writes": [{"table": "train", "columns": ["vessel_id", "functional_area_description"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mentalhealthparityviolations depends on grant\ndbt build -s mentalhealthparityviolations --vars '{\"source_table\":\"grant\"}'\n", "labels": {"reads": [{"table": "grant", "columns": null}], "writes": [{"table": "mentalhealthparityviolations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO public_schools SELECT 1\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT lawyer_name, review_date FROM redundant_billing_data LIMIT 256\")\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO cybersecurity.strategies SELECT gross_in_dollar, category_id, reporter_id FROM stock_levels WHERE gross_in_dollar > 367\")\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": ["lawyer_name", "review_date"]}, {"table": "stock_levels", "columns": ["gross_in_dollar", "category_id", "reporter_id"]}], "writes": [{"table": "cybersecurity.strategies", "columns": ["gross_in_dollar", "category_id", "reporter_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads_payments_daily (billingamount, dept_store_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads_payments_daily", "columns": ["billingamount", "dept_store_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM scan_dates\", conn)\ndf.to_sql(\"funding_rounds\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "scan_dates", "columns": null}], "writes": [{"table": "funding_rounds", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT headquarter, attack_country FROM cycling\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"artprograms\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cycling", "columns": ["headquarter", "attack_country"]}], "writes": [{"table": "artprograms", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO contributions SELECT origin, task FROM shoes WHERE origin > 368\"\n", "labels": {"reads": [{"table": "shoes", "columns": ["origin", "task"]}], "writes": [{"table": "contributions", "columns": ["origin", "task"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.exhibitionid > 187).all()\n# src table: stg.orders_daily\nengine.execute(\"INSERT INTO mart.mart_device_log_hourly SELECT * FROM stg.orders_daily\")\n", "labels": {"reads": [{"table": "stg.orders_daily", "columns": null}], "writes": [{"table": "mart.mart_device_log_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM digital_trends\"\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO waste_data SELECT a.completion_year, b.baseprice FROM dishes a JOIN document_types b ON a.review_id = b.review_id\"\n", "labels": {"reads": [{"table": "dishes", "columns": null}, {"table": "document_types", "columns": null}], "writes": [{"table": "waste_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO threat_intelligence SELECT building_full_name, hours_spent, well_id FROM articles WHERE building_full_name > 375\")\n", "labels": {"reads": [{"table": "articles", "columns": ["building_full_name", "hours_spent", "well_id"]}], "writes": [{"table": "threat_intelligence", "columns": ["building_full_name", "hours_spent", "well_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.playtime > 452).all()\n# src table: product_characteristics\nengine.execute(\"INSERT INTO coal SELECT * FROM product_characteristics\")\n", "labels": {"reads": [{"table": "product_characteristics", "columns": null}], "writes": [{"table": "coal", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nresult = value * ratio + offset\nsql = \"INSERT INTO songs_length SELECT a.mental_health_resource_access, b.virtual_tour_sessions FROM industrial_customers a JOIN authenticationlogs b ON a.product_category_description = b.product_category_description\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "industrial_customers", "columns": null}, {"table": "authenticationlogs", "columns": null}], "writes": [{"table": "songs_length", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO eco_materials (citation_time, system) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "eco_materials", "columns": ["citation_time", "system"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO unionmembers (mission_date, first_donation_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "unionmembers", "columns": ["mission_date", "first_donation_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fields_production\").toPandas()\ndf[[\"dates_active\", \"start_year\"]].to_sql(\"season_assists\", engine, index=False)\n", "labels": {"reads": [{"table": "fields_production", "columns": null}], "writes": [{"table": "season_assists", "columns": ["dates_active", "start_year"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO volunteer_events (menu_name, improvement) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "volunteer_events", "columns": ["menu_name", "improvement"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO community_education_programs SELECT organization_id, operationid FROM courts WHERE organization_id > 30\")\n", "labels": {"reads": [{"table": "courts", "columns": ["organization_id", "operationid"]}], "writes": [{"table": "community_education_programs", "columns": ["organization_id", "operationid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model esportsevents depends on sustainableprojects\ndbt run -s esportsevents --vars '{\"source_table\":\"sustainableprojects\"}'\n", "labels": {"reads": [{"table": "sustainableprojects", "columns": null}], "writes": [{"table": "esportsevents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model order_items depends on animal_rehab\ndbt build --models order_items --vars '{\"source_table\":\"animal_rehab\"}'\n", "labels": {"reads": [{"table": "animal_rehab", "columns": null}], "writes": [{"table": "order_items", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO game_sales (is_accessible, dept_store_chain_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "game_sales", "columns": ["is_accessible", "dept_store_chain_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"stores\")\nsrc.write.insertInto(\"carbon_offset_programs\", overwrite=True)\n", "labels": {"reads": [{"table": "stores", "columns": null}], "writes": [{"table": "carbon_offset_programs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO wildlife_habitats SELECT studio_name, dorm_name FROM stg.refunds_daily WHERE studio_name > 390;\nEOF\n", "labels": {"reads": [{"table": "stg.refunds_daily", "columns": ["studio_name", "dorm_name"]}], "writes": [{"table": "wildlife_habitats", "columns": ["studio_name", "dorm_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"casebilling\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"team\")\n", "labels": {"reads": [{"table": "casebilling", "columns": null}], "writes": [{"table": "team", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT velocity, intervention_type FROM injury_accident LIMIT 61\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "injury_accident", "columns": ["velocity", "intervention_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO teaches (inspection_id, max_depth) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "teaches", "columns": ["inspection_id", "max_depth"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO book SELECT airline, brand_id, health_equity_metric_3 FROM therapy_session WHERE airline > 283\"\n", "labels": {"reads": [{"table": "therapy_session", "columns": ["airline", "brand_id", "health_equity_metric_3"]}], "writes": [{"table": "book", "columns": ["airline", "brand_id", "health_equity_metric_3"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO playergamedata SELECT a.forest_type, b.fault_log_entry_id FROM landfill_capacity a JOIN labor_unions b ON a.project_date = b.project_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "landfill_capacity", "columns": null}, {"table": "labor_unions", "columns": null}], "writes": [{"table": "playergamedata", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dws.dws_events_hourly (subscriber_type, coal_reserve_remaining) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_events_hourly", "columns": ["subscriber_type", "coal_reserve_remaining"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO al_jazeera_data SELECT a.country, b.savingsid FROM open_pedagogy_courses a JOIN council_tax b ON a.end_time = b.end_time\"\n", "labels": {"reads": [{"table": "open_pedagogy_courses", "columns": null}, {"table": "council_tax", "columns": null}], "writes": [{"table": "al_jazeera_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ocean_shipping.cargo --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ocean_shipping.cargo", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM crops\", conn)\ndf.to_sql(\"cyber_incidents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "crops", "columns": null}], "writes": [{"table": "cyber_incidents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM economic_diversification\", conn)\ndf.to_sql(\"dws_events_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "economic_diversification", "columns": null}], "writes": [{"table": "dws_events_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT roomname, amount_donated FROM stg.coupon_use_delta LIMIT 371;\nINSERT INTO nutrition_facts SELECT service, don_name, group_name, address_content FROM postseason WHERE service > 422;\nSQL\n", "labels": {"reads": [{"table": "stg.coupon_use_delta", "columns": ["roomname", "amount_donated"]}, {"table": "postseason", "columns": ["service", "don_name", "group_name", "address_content"]}], "writes": [{"table": "nutrition_facts", "columns": ["service", "don_name", "group_name", "address_content"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT union_member, value_points FROM higher_ed.publications LIMIT 153\")\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO stg.refunds_daily SELECT contract_start_date, member_name, production_mwh FROM wind_energy WHERE contract_start_date > 80\")\n", "labels": {"reads": [{"table": "higher_ed.publications", "columns": ["union_member", "value_points"]}, {"table": "wind_energy", "columns": ["contract_start_date", "member_name", "production_mwh"]}], "writes": [{"table": "stg.refunds_daily", "columns": ["contract_start_date", "member_name", "production_mwh"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO communitycourtcases SELECT min_temperature_f, loan_id, species_id FROM monthly_temp WHERE min_temperature_f > 289\"\n", "labels": {"reads": [{"table": "monthly_temp", "columns": ["min_temperature_f", "loan_id", "species_id"]}], "writes": [{"table": "communitycourtcases", "columns": ["min_temperature_f", "loan_id", "species_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO medicine SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT partner_id, moisture FROM procedures\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\ndf.to_sql(\"countryintelligenceops\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "procedures", "columns": ["partner_id", "moisture"]}], "writes": [{"table": "countryintelligenceops", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT accommodationtype, secretary_vote FROM dancefunding LIMIT 385;\nINSERT INTO gradeconversion SELECT is_organic, insurancetype, undergraduate, shariah_compliant_investment_amount FROM artwork_styles WHERE is_organic > 125;\nSQL\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["accommodationtype", "secretary_vote"]}, {"table": "artwork_styles", "columns": ["is_organic", "insurancetype", "undergraduate", "shariah_compliant_investment_amount"]}], "writes": [{"table": "gradeconversion", "columns": ["is_organic", "insurancetype", "undergraduate", "shariah_compliant_investment_amount"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table sustainability_metrics --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO exhibition_visits SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"products_in_events\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "products_in_events", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO route SELECT creator, mean_visibility_miles, founders FROM mart.mart_shipments_hourly WHERE creator > 111\"\n", "labels": {"reads": [{"table": "mart.mart_shipments_hourly", "columns": ["creator", "mean_visibility_miles", "founders"]}], "writes": [{"table": "route", "columns": ["creator", "mean_visibility_miles", "founders"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT total_budget_percent_invested, category FROM temperaturehistory LIMIT 198\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "temperaturehistory", "columns": ["total_budget_percent_invested", "category"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model electricvehicleadoption depends on languagesatrisk\ndbt build --models electricvehicleadoption --vars '{\"source_table\":\"languagesatrisk\"}'\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": null}], "writes": [{"table": "electricvehicleadoption", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"factory_workers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ads.ads_risk_score_hourly\")\n", "labels": {"reads": [{"table": "factory_workers", "columns": null}], "writes": [{"table": "ads.ads_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM investor_activities\", conn)\ndf.to_sql(\"film_market_estimation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "investor_activities", "columns": null}], "writes": [{"table": "film_market_estimation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO community_health_centers SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO distributors SELECT ai_algorithm_id, dishid FROM dwd.users_daily WHERE ai_algorithm_id > 69\"\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": ["ai_algorithm_id", "dishid"]}], "writes": [{"table": "distributors", "columns": ["ai_algorithm_id", "dishid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO counties SELECT 1\"\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT user_name, warehouse_id FROM discount_coupons LIMIT 120\")\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO shop SELECT meal_date, builder FROM communitypolicing WHERE meal_date > 156\")\n", "labels": {"reads": [{"table": "discount_coupons", "columns": ["user_name", "warehouse_id"]}, {"table": "communitypolicing", "columns": ["meal_date", "builder"]}], "writes": [{"table": "shop", "columns": ["meal_date", "builder"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT region_name, organized_by FROM takes LIMIT 226\")\nimport logging\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO lots SELECT quality_rank, stateid, account_balance, formats FROM coowners WHERE quality_rank > 447\")\n", "labels": {"reads": [{"table": "takes", "columns": ["region_name", "organized_by"]}, {"table": "coowners", "columns": ["quality_rank", "stateid", "account_balance", "formats"]}], "writes": [{"table": "lots", "columns": ["quality_rank", "stateid", "account_balance", "formats"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO publication (vehicle_model, addressid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "publication", "columns": ["vehicle_model", "addressid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dw.dw_member_point_hourly\"\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO caribbean_tourists SELECT a.catalog_level_number, b.station FROM ads_orders a JOIN candidates b ON a.station_name = b.station_name\"\n", "labels": {"reads": [{"table": "ads_orders", "columns": null}, {"table": "candidates", "columns": null}], "writes": [{"table": "caribbean_tourists", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"defense_contractors\")\nsrc.write.insertInto(\"gradeconversion\", overwrite=True)\n", "labels": {"reads": [{"table": "defense_contractors", "columns": null}], "writes": [{"table": "gradeconversion", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO archaeologists SELECT issue_month, well_name, duration, depth FROM coach WHERE issue_month > 120;\nEOF\n", "labels": {"reads": [{"table": "coach", "columns": ["issue_month", "well_name", "duration", "depth"]}], "writes": [{"table": "archaeologists", "columns": ["issue_month", "well_name", "duration", "depth"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"red_line\").toPandas()\ndf[[\"document_status_code\", \"postalcode\"]].to_sql(\"foodsafetyrecords\", engine, index=False)\n", "labels": {"reads": [{"table": "red_line", "columns": null}], "writes": [{"table": "foodsafetyrecords", "columns": ["document_status_code", "postalcode"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ethical_ai SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model caribbeansea depends on regulatory_frameworks\ndbt build --models caribbeansea --vars 'source: regulatory_frameworks'\n", "labels": {"reads": [{"table": "regulatory_frameworks", "columns": null}], "writes": [{"table": "caribbeansea", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model specieswatertemp depends on device_accessibility\ndbt run -s specieswatertemp --vars '{\"source_table\":\"device_accessibility\"}'\n", "labels": {"reads": [{"table": "device_accessibility", "columns": null}], "writes": [{"table": "specieswatertemp", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"solana_transactions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"marine_species_indian\")\n", "labels": {"reads": [{"table": "solana_transactions", "columns": null}], "writes": [{"table": "marine_species_indian", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO defense_diplomacy SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.charging_level > 496).all()\n# src table: players\nengine.execute(\"INSERT INTO states SELECT * FROM players\")\n", "labels": {"reads": [{"table": "players", "columns": null}], "writes": [{"table": "states", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ods.member_point_df SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.total_amount_purchased > 491).all()\n# src table: apartments\nengine.execute(\"INSERT INTO dws_coupon_use SELECT * FROM apartments\")\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": [{"table": "dws_coupon_use", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO teacher_development_race SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT monthlyactiveusers, extractiondate FROM retailers LIMIT 22;\nINSERT INTO sales_by_quarter SELECT uses_vr, join_year FROM opioid_overdoses WHERE uses_vr > 58;\nSQL\n", "labels": {"reads": [{"table": "retailers", "columns": ["monthlyactiveusers", "extractiondate"]}, {"table": "opioid_overdoses", "columns": ["uses_vr", "join_year"]}], "writes": [{"table": "sales_by_quarter", "columns": ["uses_vr", "join_year"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO agro_regions SELECT customer_code, claim_date FROM gamegenres WHERE customer_code > 60\"\n", "labels": {"reads": [{"table": "gamegenres", "columns": ["customer_code", "claim_date"]}], "writes": [{"table": "agro_regions", "columns": ["customer_code", "claim_date"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO cargos (payment_id, activity_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "cargos", "columns": ["payment_id", "activity_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"decentralized_applications\")\nsrc.write.insertInto(\"vulnerabilities\", overwrite=True)\n", "labels": {"reads": [{"table": "decentralized_applications", "columns": null}], "writes": [{"table": "vulnerabilities", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"document_types\")\nsrc.write.insertInto(\"artworks\", overwrite=True)\n", "labels": {"reads": [{"table": "document_types", "columns": null}], "writes": [{"table": "artworks", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO criminalcases (eventdate, project_details) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "criminalcases", "columns": ["eventdate", "project_details"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"hospitals\")\npush_to_output(df, \"gamegenres\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "hospitals", "columns": null}], "writes": [{"table": "gamegenres", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT violation_id, productivity FROM educators LIMIT 197;\nINSERT INTO foodsafetyrecords SELECT portid, practice, claim_outcome_code FROM canada_cosmetics_preferences WHERE portid > 279;\nSQL\n", "labels": {"reads": [{"table": "educators", "columns": ["violation_id", "productivity"]}, {"table": "canada_cosmetics_preferences", "columns": ["portid", "practice", "claim_outcome_code"]}], "writes": [{"table": "foodsafetyrecords", "columns": ["portid", "practice", "claim_outcome_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO distributors (volunteerjoindate, booked_count) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "distributors", "columns": ["volunteerjoindate", "booked_count"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_shipments_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dws.dws_orders_full\")\n", "labels": {"reads": [{"table": "dws.dws_shipments_full", "columns": null}], "writes": [{"table": "dws.dws_orders_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table machines --target-dir /tmp/land\n", "labels": {"reads": [{"table": "machines", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO yoga SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO request SELECT heritage_site_id, blockcode, passenger_count, physician FROM garmentproduction WHERE heritage_site_id > 331\"], check=True)\n", "labels": {"reads": [{"table": "garmentproduction", "columns": ["heritage_site_id", "blockcode", "passenger_count", "physician"]}], "writes": [{"table": "request", "columns": ["heritage_site_id", "blockcode", "passenger_count", "physician"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT purchase_details, investment_id FROM mart_shipments_full LIMIT 53;\nINSERT INTO product_details SELECT healthcareid, ngo_name, framework FROM students WHERE healthcareid > 408;\nSQL\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": ["purchase_details", "investment_id"]}, {"table": "students", "columns": ["healthcareid", "ngo_name", "framework"]}], "writes": [{"table": "product_details", "columns": ["healthcareid", "ngo_name", "framework"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ethical_ai\").toPandas()\ndf[[\"heritage_site_id\", \"payment_type_code\"]].to_sql(\"territory.human_rights_data\", engine, index=False)\n", "labels": {"reads": [{"table": "ethical_ai", "columns": null}], "writes": [{"table": "territory.human_rights_data", "columns": ["heritage_site_id", "payment_type_code"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"average\")\nsrc.write.insertInto(\"ref_budget_codes\", overwrite=True)\n", "labels": {"reads": [{"table": "average", "columns": null}], "writes": [{"table": "ref_budget_codes", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ads.ads_payments SELECT a.shipment_date, b.retailer FROM public.police_calls a JOIN public_transport.passenger_count b ON a.ll_hours = b.ll_hours\"\n", "labels": {"reads": [{"table": "public.police_calls", "columns": null}, {"table": "public_transport.passenger_count", "columns": null}], "writes": [{"table": "ads.ads_payments", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT zipcode, doctorsper1000 FROM landfill_capacity_north_america LIMIT 353\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO companies_extended SELECT news_story_id, energy_efficiency_savings FROM people WHERE news_story_id > 134\")\n", "labels": {"reads": [{"table": "landfill_capacity_north_america", "columns": ["zipcode", "doctorsper1000"]}, {"table": "people", "columns": ["news_story_id", "energy_efficiency_savings"]}], "writes": [{"table": "companies_extended", "columns": ["news_story_id", "energy_efficiency_savings"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO donationprograms SELECT domestic_passengers, reported FROM winter_olympics WHERE domestic_passengers > 93;\nEOF\n", "labels": {"reads": [{"table": "winter_olympics", "columns": ["domestic_passengers", "reported"]}], "writes": [{"table": "donationprograms", "columns": ["domestic_passengers", "reported"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.arrival_time > 389).all()\n# src table: part_faults\nengine.execute(\"INSERT INTO stock_levels SELECT * FROM part_faults\")\n", "labels": {"reads": [{"table": "part_faults", "columns": null}], "writes": [{"table": "stock_levels", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_projects\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO taxi_data SELECT fleet_series, white, book_id, sales_amount FROM safety_incidents_india WHERE fleet_series > 391\"\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": ["fleet_series", "white", "book_id", "sales_amount"]}], "writes": [{"table": "taxi_data", "columns": ["fleet_series", "white", "book_id", "sales_amount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table dw.dw_sessions_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw.dw_sessions_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"digital_divide_initiatives\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"community_health_workers\")\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": null}], "writes": [{"table": "community_health_workers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT volunteerage, recycled FROM displaced_people LIMIT 341\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO loan SELECT transit_passengers, num_beds, researcher_id FROM media_library WHERE transit_passengers > 413\")\n", "labels": {"reads": [{"table": "displaced_people", "columns": ["volunteerage", "recycled"]}, {"table": "media_library", "columns": ["transit_passengers", "num_beds", "researcher_id"]}], "writes": [{"table": "loan", "columns": ["transit_passengers", "num_beds", "researcher_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT monthly_rental, apt_id FROM open_data_initiatives LIMIT 249\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO characteristics SELECT statement_id, mission_name, hospitalid FROM mart_shipments_full WHERE statement_id > 126\")\n", "labels": {"reads": [{"table": "open_data_initiatives", "columns": ["monthly_rental", "apt_id"]}, {"table": "mart_shipments_full", "columns": ["statement_id", "mission_name", "hospitalid"]}], "writes": [{"table": "characteristics", "columns": ["statement_id", "mission_name", "hospitalid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthcare_centers\").toPandas()\ndf[[\"appointment_date\", \"market\"]].to_sql(\"subjects\", engine, index=False)\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": null}], "writes": [{"table": "subjects", "columns": ["appointment_date", "market"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model fair_trade_suppliers depends on salesdata\ndbt run --select fair_trade_suppliers --vars 'source: salesdata'\n", "labels": {"reads": [{"table": "salesdata", "columns": null}], "writes": [{"table": "fair_trade_suppliers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO school_enrollment SELECT operation_type, galleryname FROM lenders WHERE operation_type > 439\"\n", "labels": {"reads": [{"table": "lenders", "columns": ["operation_type", "galleryname"]}], "writes": [{"table": "school_enrollment", "columns": ["operation_type", "galleryname"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT product_description, employment_id FROM eu_humanitarian_assistance LIMIT 357\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "eu_humanitarian_assistance", "columns": ["product_description", "employment_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.launch_date > 354).all()\n# src table: defense_projects_sales\nengine.execute(\"INSERT INTO stg.campaigns_daily SELECT * FROM defense_projects_sales\")\n", "labels": {"reads": [{"table": "defense_projects_sales", "columns": null}], "writes": [{"table": "stg.campaigns_daily", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dws.coupon_use_di SELECT activity_type, overall_rating, saleamount FROM emergencyservices WHERE activity_type > 340\"\n", "labels": {"reads": [{"table": "emergencyservices", "columns": ["activity_type", "overall_rating", "saleamount"]}], "writes": [{"table": "dws.coupon_use_di", "columns": ["activity_type", "overall_rating", "saleamount"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"submersible_dives\").toPandas()\ndf[[\"number_deaths\", \"daily_co2_emission\"]].to_sql(\"genetics_stats.research_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "submersible_dives", "columns": null}], "writes": [{"table": "genetics_stats.research_projects", "columns": ["number_deaths", "daily_co2_emission"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fare_collection\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tech_accessibility_funding\")\n", "labels": {"reads": [{"table": "fare_collection", "columns": null}], "writes": [{"table": "tech_accessibility_funding", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO trainers SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table product_reviews --target-dir /tmp/land\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods_exposure_delta SELECT security_level, athlete_name FROM material WHERE security_level > 385;\nEOF\n", "labels": {"reads": [{"table": "material", "columns": ["security_level", "athlete_name"]}], "writes": [{"table": "ods_exposure_delta", "columns": ["security_level", "athlete_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO military_equipment SELECT hub_id, company_name FROM recyclingrates WHERE hub_id > 305\"\n", "labels": {"reads": [{"table": "recyclingrates", "columns": ["hub_id", "company_name"]}], "writes": [{"table": "military_equipment", "columns": ["hub_id", "company_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO middle_east_military_spending SELECT a.structure_type, b.city_id FROM student_courses a JOIN plankton b ON a.humidity = b.humidity\"\n", "labels": {"reads": [{"table": "student_courses", "columns": null}, {"table": "plankton", "columns": null}], "writes": [{"table": "middle_east_military_spending", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO medical_professionals SELECT objectnumber, region_code, star_rating_code, follow_up_date FROM patient WHERE objectnumber > 218\"], check=True)\n", "labels": {"reads": [{"table": "patient", "columns": ["objectnumber", "region_code", "star_rating_code", "follow_up_date"]}], "writes": [{"table": "medical_professionals", "columns": ["objectnumber", "region_code", "star_rating_code", "follow_up_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attorney_billing_rates\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"cb_agreements\")\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": null}], "writes": [{"table": "cb_agreements", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table media_library --target-dir /tmp/land\n", "labels": {"reads": [{"table": "media_library", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO esportsevents SELECT paper_id, ethical_manufacturing FROM university WHERE paper_id > 44\"\n", "labels": {"reads": [{"table": "university", "columns": ["paper_id", "ethical_manufacturing"]}], "writes": [{"table": "esportsevents", "columns": ["paper_id", "ethical_manufacturing"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws.cart_item_di\", conn)\ndf.to_sql(\"ais\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws.cart_item_di", "columns": null}], "writes": [{"table": "ais", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT athlete_id, nid FROM mart.device_log_hourly LIMIT 187\")\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO producesupplier SELECT price_in_euros, governor, project FROM satellites_by_country WHERE price_in_euros > 481\")\n", "labels": {"reads": [{"table": "mart.device_log_hourly", "columns": ["athlete_id", "nid"]}, {"table": "satellites_by_country", "columns": ["price_in_euros", "governor", "project"]}], "writes": [{"table": "producesupplier", "columns": ["price_in_euros", "governor", "project"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artists_valuation\").toPandas()\ndf[[\"adults\", \"ethnicity\"]].to_sql(\"facility\", engine, index=False)\n", "labels": {"reads": [{"table": "artists_valuation", "columns": null}], "writes": [{"table": "facility", "columns": ["adults", "ethnicity"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO suppliersfairlabor SELECT zone_id, black FROM dws.exposure WHERE zone_id > 208\"\n", "labels": {"reads": [{"table": "dws.exposure", "columns": ["zone_id", "black"]}], "writes": [{"table": "suppliersfairlabor", "columns": ["zone_id", "black"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dw_users_full\", conn)\ndf.to_sql(\"ads.users_full\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dw_users_full", "columns": null}], "writes": [{"table": "ads.users_full", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT conferencename, exhibitions FROM mart_payments_df\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"dwd_sessions_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "mart_payments_df", "columns": ["conferencename", "exhibitions"]}], "writes": [{"table": "dwd_sessions_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO projecttimeline SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO food_justice SELECT surname, problem_log_id, opening_year FROM volunteerprograms WHERE surname > 2;\nEOF\n", "labels": {"reads": [{"table": "volunteerprograms", "columns": ["surname", "problem_log_id", "opening_year"]}], "writes": [{"table": "food_justice", "columns": ["surname", "problem_log_id", "opening_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO criminal_justice_reform_initiatives SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT supplier, stationname FROM defenseprojects LIMIT 220;\nINSERT INTO cultivators SELECT weight, client FROM offender_demographics WHERE weight > 214;\nSQL\n", "labels": {"reads": [{"table": "defenseprojects", "columns": ["supplier", "stationname"]}, {"table": "offender_demographics", "columns": ["weight", "client"]}], "writes": [{"table": "cultivators", "columns": ["weight", "client"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ods.ods_exposure_delta SELECT practices, bedroom_count FROM equipment_maintenance WHERE practices > 57;\nEOF\n", "labels": {"reads": [{"table": "equipment_maintenance", "columns": ["practices", "bedroom_count"]}], "writes": [{"table": "ods.ods_exposure_delta", "columns": ["practices", "bedroom_count"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM haircare_cruelty\"\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO journal_committee (fda_approved, exploited) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "journal_committee", "columns": ["fda_approved", "exploited"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"head\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"criminal_cases\")\n", "labels": {"reads": [{"table": "head", "columns": null}], "writes": [{"table": "criminal_cases", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO mart.mart_risk_score_hourly SELECT exhibition_id, retail_price FROM gameplatforms WHERE exhibition_id > 70\")\n", "labels": {"reads": [{"table": "gameplatforms", "columns": ["exhibition_id", "retail_price"]}], "writes": [{"table": "mart.mart_risk_score_hourly", "columns": ["exhibition_id", "retail_price"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO sustainability_metrics SELECT organization, organisation_details FROM unesco_intangible_heritage WHERE organization > 343\"\n", "labels": {"reads": [{"table": "unesco_intangible_heritage", "columns": ["organization", "organisation_details"]}], "writes": [{"table": "sustainability_metrics", "columns": ["organization", "organisation_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.document_status_code > 468).all()\n# src table: zip_codes\nengine.execute(\"INSERT INTO building_stats SELECT * FROM zip_codes\")\n", "labels": {"reads": [{"table": "zip_codes", "columns": null}], "writes": [{"table": "building_stats", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO market SELECT a.visit_id, b.gross_worldwide FROM customer_policies a JOIN stateinfrastructure b ON a.cmi_cross_ref_id = b.cmi_cross_ref_id\"\n", "labels": {"reads": [{"table": "customer_policies", "columns": null}, {"table": "stateinfrastructure", "columns": null}], "writes": [{"table": "market", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO healthcare_centers SELECT mission_date, typical_buying_price, exhibitionname FROM fraud_detections WHERE mission_date > 67;\nEOF\n", "labels": {"reads": [{"table": "fraud_detections", "columns": ["mission_date", "typical_buying_price", "exhibitionname"]}], "writes": [{"table": "healthcare_centers", "columns": ["mission_date", "typical_buying_price", "exhibitionname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ancient_artifacts SELECT * FROM legacy\ncur.execute(\"SELECT cases_count, age_group_id FROM ads_cart_item_hourly LIMIT 407\")\n", "labels": {"reads": [{"table": "ads_cart_item_hourly", "columns": ["cases_count", "age_group_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sustainabilityid, acc_regular_season FROM flu_shots LIMIT 268\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "flu_shots", "columns": ["sustainabilityid", "acc_regular_season"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"mart.coupon_use_hourly\")\nexport_to_target(df, \"online_platform\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.coupon_use_hourly", "columns": null}], "writes": [{"table": "online_platform", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM machinery\", conn)\ndf.to_sql(\"agriculturalinvestments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": [{"table": "agriculturalinvestments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO testtypes SELECT journal_id, institution, risk_level, labordate FROM projecttimeline WHERE journal_id > 142\"\n", "labels": {"reads": [{"table": "projecttimeline", "columns": ["journal_id", "institution", "risk_level", "labordate"]}], "writes": [{"table": "testtypes", "columns": ["journal_id", "institution", "risk_level", "labordate"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO distributors SELECT category, thing_id FROM aquatic_farms WHERE category > 153\"\n", "labels": {"reads": [{"table": "aquatic_farms", "columns": ["category", "thing_id"]}], "writes": [{"table": "distributors", "columns": ["category", "thing_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO chemical_composition (cargoid, venue) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "chemical_composition", "columns": ["cargoid", "venue"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO city_budgets SELECT tourists, funding FROM waste_types WHERE tourists > 85\"\n", "labels": {"reads": [{"table": "waste_types", "columns": ["tourists", "funding"]}], "writes": [{"table": "city_budgets", "columns": ["tourists", "funding"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.parameters > 239).all()\n# src table: train_station\nengine.execute(\"INSERT INTO fishcaught SELECT * FROM train_station\")\n", "labels": {"reads": [{"table": "train_station", "columns": null}], "writes": [{"table": "fishcaught", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mart.mart_member_point_hourly SELECT a.session_date, b.employeename FROM conditions a JOIN climber b ON a.principal_activities = b.principal_activities\"\n", "labels": {"reads": [{"table": "conditions", "columns": null}, {"table": "climber", "columns": null}], "writes": [{"table": "mart.mart_member_point_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model drivers depends on stg.coupon_use\ndbt build -s drivers --vars '{\"src\":\"stg.coupon_use\"}'\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": null}], "writes": [{"table": "drivers", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO workforce (average, condition) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "workforce", "columns": ["average", "condition"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO engineer_visits SELECT last_year, treatment_date, reign FROM sustainableprojects WHERE last_year > 18\"], check=True)\n", "labels": {"reads": [{"table": "sustainableprojects", "columns": ["last_year", "treatment_date", "reign"]}], "writes": [{"table": "engineer_visits", "columns": ["last_year", "treatment_date", "reign"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table grapes --columns contract_type,route_short_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "grapes", "columns": ["contract_type", "route_short_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table ads.ads_cart_item_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads.ads_cart_item_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO lenders SELECT a.fda_approved, b.date_valid_to FROM rebounds a JOIN worker_union b ON a.district = b.district\"\n", "labels": {"reads": [{"table": "rebounds", "columns": null}, {"table": "worker_union", "columns": null}], "writes": [{"table": "lenders", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM european_healthcare\", conn)\ndf.to_sql(\"soccer_teams\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "european_healthcare", "columns": null}], "writes": [{"table": "soccer_teams", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT profits_billion, restaurant_id FROM dwd.dwd_risk_score_delta LIMIT 450\")\nimport logging\nspark.sql(\"INSERT INTO marine_species_arctic_ocean SELECT practiceid, reports_to FROM passengers WHERE practiceid > 352\")\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": ["profits_billion", "restaurant_id"]}, {"table": "passengers", "columns": ["practiceid", "reports_to"]}], "writes": [{"table": "marine_species_arctic_ocean", "columns": ["practiceid", "reports_to"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd.dwd_member_point_di SELECT outcome_id, completed, digital_channel, vessel_id FROM circular_economy_initiatives WHERE outcome_id > 443\"], check=True)\n", "labels": {"reads": [{"table": "circular_economy_initiatives", "columns": ["outcome_id", "completed", "digital_channel", "vessel_id"]}], "writes": [{"table": "dwd.dwd_member_point_di", "columns": ["outcome_id", "completed", "digital_channel", "vessel_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO circular_economy_companies SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO facility_production SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO environmentalimpact SELECT num_pallets, socialimpactscore, county_name, community_name FROM legalaidrequests WHERE num_pallets > 420\"], check=True)\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": ["num_pallets", "socialimpactscore", "county_name", "community_name"]}], "writes": [{"table": "environmentalimpact", "columns": ["num_pallets", "socialimpactscore", "county_name", "community_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO shark_biomass SELECT a.people_id, b.visitid FROM rental a JOIN lenders b ON a.destination_id = b.destination_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "rental", "columns": null}, {"table": "lenders", "columns": null}], "writes": [{"table": "shark_biomass", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO design_standards SELECT is_autonomous, purchase_date, book_club_id FROM sustainability_metrics WHERE is_autonomous > 160;\nEOF\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": ["is_autonomous", "purchase_date", "book_club_id"]}], "writes": [{"table": "design_standards", "columns": ["is_autonomous", "purchase_date", "book_club_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO european_healthcare SELECT start_time, carrierid FROM resilience_infrastructure WHERE start_time > 438\"\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": ["start_time", "carrierid"]}], "writes": [{"table": "european_healthcare", "columns": ["start_time", "carrierid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO reviews (chw_id, detected_at) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "reviews", "columns": ["chw_id", "detected_at"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT book_title, impactid FROM london.stations\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"visitor_statistics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "london.stations", "columns": ["book_title", "impactid"]}], "writes": [{"table": "visitor_statistics", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO therapy_attendance SELECT speed, bridgetype, port_name FROM biosensors.readings WHERE speed > 2\"\n", "labels": {"reads": [{"table": "biosensors.readings", "columns": ["speed", "bridgetype", "port_name"]}], "writes": [{"table": "therapy_attendance", "columns": ["speed", "bridgetype", "port_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO obesity SELECT jan, updatedate, co_owner_count FROM climber WHERE jan > 121\")\n", "labels": {"reads": [{"table": "climber", "columns": ["jan", "updatedate", "co_owner_count"]}], "writes": [{"table": "obesity", "columns": ["jan", "updatedate", "co_owner_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO phishing_targets SELECT trips, policy_name, album_id, artifactname FROM restorative_justice_center WHERE trips > 222;\nEOF\n", "labels": {"reads": [{"table": "restorative_justice_center", "columns": ["trips", "policy_name", "album_id", "artifactname"]}], "writes": [{"table": "phishing_targets", "columns": ["trips", "policy_name", "album_id", "artifactname"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"ads.ads_vendors_hourly\")\ndump_to_sink(df, \"city_budgets\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.ads_vendors_hourly", "columns": null}], "writes": [{"table": "city_budgets", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artpieces\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "artpieces", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table flu_cases --columns production_budget,id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "flu_cases", "columns": ["production_budget", "id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"nursing_homes\")\nsrc.write.insertInto(\"stg.member_point_df\", overwrite=True)\n", "labels": {"reads": [{"table": "nursing_homes", "columns": null}], "writes": [{"table": "stg.member_point_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dw.dw_products_delta SELECT vesselname, highscore, promotiondate FROM ods_payments_delta WHERE vesselname > 26\"], check=True)\n", "labels": {"reads": [{"table": "ods_payments_delta", "columns": ["vesselname", "highscore", "promotiondate"]}], "writes": [{"table": "dw.dw_products_delta", "columns": ["vesselname", "highscore", "promotiondate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO price_data SELECT a.transportation_method, b.neighborhoodid FROM stores_2 a JOIN lives_in b ON a.trend = b.trend\"\n", "labels": {"reads": [{"table": "stores_2", "columns": null}, {"table": "lives_in", "columns": null}], "writes": [{"table": "price_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO recycling_rates_state (crs_credit, count) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "recycling_rates_state", "columns": ["crs_credit", "count"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO heart_rate_data SELECT testtypeid, cityid, treatment_date FROM healthequitymetrics WHERE testtypeid > 243\"\n", "labels": {"reads": [{"table": "healthequitymetrics", "columns": ["testtypeid", "cityid", "treatment_date"]}], "writes": [{"table": "heart_rate_data", "columns": ["testtypeid", "cityid", "treatment_date"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.dws_users_hourly (class_president_vote, start_station_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_users_hourly", "columns": ["class_president_vote", "start_station_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO gymnast SELECT regionid, cropname, ship_id FROM coralreefs WHERE regionid > 258\"\n", "labels": {"reads": [{"table": "coralreefs", "columns": ["regionid", "cropname", "ship_id"]}], "writes": [{"table": "gymnast", "columns": ["regionid", "cropname", "ship_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM functional_areas\"\n", "labels": {"reads": [{"table": "functional_areas", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO fruitimport SELECT document_name, personnelid, destinationid FROM movie_ratings WHERE document_name > 499\"\n", "labels": {"reads": [{"table": "movie_ratings", "columns": ["document_name", "personnelid", "destinationid"]}], "writes": [{"table": "fruitimport", "columns": ["document_name", "personnelid", "destinationid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO marinespeciesobservations SELECT campaign_name, intervention_type FROM navalvessels WHERE campaign_name > 159;\nEOF\n", "labels": {"reads": [{"table": "navalvessels", "columns": ["campaign_name", "intervention_type"]}], "writes": [{"table": "marinespeciesobservations", "columns": ["campaign_name", "intervention_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dws.dws_inventory_di SELECT product_type, import_country, labor_practice FROM founder WHERE product_type > 54\"\n", "labels": {"reads": [{"table": "founder", "columns": ["product_type", "import_country", "labor_practice"]}], "writes": [{"table": "dws.dws_inventory_di", "columns": ["product_type", "import_country", "labor_practice"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"festivals\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "festivals", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO cycling SELECT bridgetype, labor_id FROM ads.ads_exposure_di WHERE bridgetype > 476\"\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["bridgetype", "labor_id"]}], "writes": [{"table": "cycling", "columns": ["bridgetype", "labor_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table city_department --target-dir /tmp/land\n", "labels": {"reads": [{"table": "city_department", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO regular_order_products SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO vehicle_registrations SELECT a.nid, b.u_id FROM community_leaders a JOIN tourist_attraction_features b ON a.product_stock_number = b.product_stock_number\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "community_leaders", "columns": null}, {"table": "tourist_attraction_features", "columns": null}], "writes": [{"table": "vehicle_registrations", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model cycling depends on apartments\ndbt run --models cycling --vars 'source: apartments'\n", "labels": {"reads": [{"table": "apartments", "columns": null}], "writes": [{"table": "cycling", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model item_prices depends on device_usage\ndbt build --models item_prices --vars '{\"src\":\"device_usage\"}'\n", "labels": {"reads": [{"table": "device_usage", "columns": null}], "writes": [{"table": "item_prices", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO production_sites SELECT claimtype, plant_name, acc_percent FROM vehicle_data WHERE claimtype > 135;\nEOF\n", "labels": {"reads": [{"table": "vehicle_data", "columns": ["claimtype", "plant_name", "acc_percent"]}], "writes": [{"table": "production_sites", "columns": ["claimtype", "plant_name", "acc_percent"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM whale_sightings\"\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.is_recycled > 468).all()\n# src table: vehicle_data\nengine.execute(\"INSERT INTO drug_approvals SELECT * FROM vehicle_data\")\n", "labels": {"reads": [{"table": "vehicle_data", "columns": null}], "writes": [{"table": "drug_approvals", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO defense_diplomacy SELECT policyid, last_year, credits, employee_id FROM co_ownership WHERE policyid > 412;\nEOF\n", "labels": {"reads": [{"table": "co_ownership", "columns": ["policyid", "last_year", "credits", "employee_id"]}], "writes": [{"table": "defense_diplomacy", "columns": ["policyid", "last_year", "credits", "employee_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO movie_financials SELECT co2_emissions, crs_description FROM tencel_sources WHERE co2_emissions > 286\"\n", "labels": {"reads": [{"table": "tencel_sources", "columns": ["co2_emissions", "crs_description"]}], "writes": [{"table": "movie_financials", "columns": ["co2_emissions", "crs_description"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"travel_advisory\").toPandas()\ndf[[\"claimdate\", \"clicks\"]].to_sql(\"safety_violations\", engine, index=False)\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "safety_violations", "columns": ["claimdate", "clicks"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO offender_demographics SELECT gameid, parent_organization_id, donor_name FROM mart.mart_vendors WHERE gameid > 105;\nEOF\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": ["gameid", "parent_organization_id", "donor_name"]}], "writes": [{"table": "offender_demographics", "columns": ["gameid", "parent_organization_id", "donor_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO medals SELECT f_id, launch_date, vessel_id FROM neighborhoods WHERE f_id > 52\"\n", "labels": {"reads": [{"table": "neighborhoods", "columns": ["f_id", "launch_date", "vessel_id"]}], "writes": [{"table": "medals", "columns": ["f_id", "launch_date", "vessel_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"areas\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "areas", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO communities SELECT price_in_dollar, genderid, competition_type FROM competition WHERE price_in_dollar > 101\"], check=True)\n", "labels": {"reads": [{"table": "competition", "columns": ["price_in_dollar", "genderid", "competition_type"]}], "writes": [{"table": "communities", "columns": ["price_in_dollar", "genderid", "competition_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.device_log_hourly\"\n", "labels": {"reads": [{"table": "bi.device_log_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.dwd_campaigns (vrgameid, founder_identity) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_campaigns", "columns": ["vrgameid", "founder_identity"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"shipment_data\").toPandas()\ndf[[\"cause_id\", \"exhibit_location\"]].to_sql(\"timed_status_of_things\", engine, index=False)\n", "labels": {"reads": [{"table": "shipment_data", "columns": null}], "writes": [{"table": "timed_status_of_things", "columns": ["cause_id", "exhibit_location"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM plays_games\", conn)\ndf.to_sql(\"space_debris\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "plays_games", "columns": null}], "writes": [{"table": "space_debris", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT digital_asset, supplychainid FROM attorney_billing LIMIT 162;\nINSERT INTO climate_adaptation_re SELECT exhibitionname, amount_due, staystart FROM movie_financials WHERE exhibitionname > 418;\nSQL\n", "labels": {"reads": [{"table": "attorney_billing", "columns": ["digital_asset", "supplychainid"]}, {"table": "movie_financials", "columns": ["exhibitionname", "amount_due", "staystart"]}], "writes": [{"table": "climate_adaptation_re", "columns": ["exhibitionname", "amount_due", "staystart"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"bi.clicks_hourly\")\nsrc.write.insertInto(\"bi.risk_score_df\", overwrite=True)\n", "labels": {"reads": [{"table": "bi.clicks_hourly", "columns": null}], "writes": [{"table": "bi.risk_score_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO communication_scores (district_name, dept_store_chain_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "communication_scores", "columns": ["district_name", "dept_store_chain_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT monthly_rental, safety_id FROM geological_survey LIMIT 484\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "geological_survey", "columns": ["monthly_rental", "safety_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"community_development.transactions\")\nsink_to_target(df, \"supportprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "community_development.transactions", "columns": null}], "writes": [{"table": "supportprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO player_demographics SELECT headquarter, forename, province_id FROM results WHERE headquarter > 313\"\n", "labels": {"reads": [{"table": "results", "columns": ["headquarter", "forename", "province_id"]}], "writes": [{"table": "player_demographics", "columns": ["headquarter", "forename", "province_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ods.clicks_full SELECT * FROM legacy\ncur.execute(\"SELECT habitat_name, party_email FROM dws.inventory_daily LIMIT 166\")\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": ["habitat_name", "party_email"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table states --columns mission_count,researcher --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "states", "columns": ["mission_count", "researcher"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO virtual_tourism SELECT catalog_id, mining_operation, device_id FROM economic_diversification_projects WHERE catalog_id > 486;\nEOF\n", "labels": {"reads": [{"table": "economic_diversification_projects", "columns": ["catalog_id", "mining_operation", "device_id"]}], "writes": [{"table": "virtual_tourism", "columns": ["catalog_id", "mining_operation", "device_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT water_depth, spending FROM venture LIMIT 44\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [{"table": "venture", "columns": ["water_depth", "spending"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contributiondate, customer_address_id FROM medical_professionals LIMIT 444\")\nimport logging\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO marine_life_data SELECT outcome, claim_amount, email FROM student_addresses WHERE outcome > 200\")\n", "labels": {"reads": [{"table": "medical_professionals", "columns": ["contributiondate", "customer_address_id"]}, {"table": "student_addresses", "columns": ["outcome", "claim_amount", "email"]}], "writes": [{"table": "marine_life_data", "columns": ["outcome", "claim_amount", "email"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT wellbeing_score, program_expenses FROM participants_in_events\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"ingredients\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "participants_in_events", "columns": ["wellbeing_score", "program_expenses"]}], "writes": [{"table": "ingredients", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"online_platform\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"worker_union\")\n", "labels": {"reads": [{"table": "online_platform", "columns": null}], "writes": [{"table": "worker_union", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rent_arrears\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"civilcases\")\n", "labels": {"reads": [{"table": "rent_arrears", "columns": null}], "writes": [{"table": "civilcases", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_events_full\").toPandas()\ndf[[\"project_type\", \"line_1_number_building\"]].to_sql(\"healthequitymetrics\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": [{"table": "healthequitymetrics", "columns": ["project_type", "line_1_number_building"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT screening_id, shipped_date FROM grant LIMIT 382;\nINSERT INTO platformg SELECT date_claim_made, claim_date, playtime, method_id FROM ma_inspections WHERE date_claim_made > 91;\nSQL\n", "labels": {"reads": [{"table": "grant", "columns": ["screening_id", "shipped_date"]}, {"table": "ma_inspections", "columns": ["date_claim_made", "claim_date", "playtime", "method_id"]}], "writes": [{"table": "platformg", "columns": ["date_claim_made", "claim_date", "playtime", "method_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT numcases, cultural_significance FROM project_timelines LIMIT 201\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO company SELECT connection, breed FROM broadband_providers WHERE connection > 270\")\n", "labels": {"reads": [{"table": "project_timelines", "columns": ["numcases", "cultural_significance"]}, {"table": "broadband_providers", "columns": ["connection", "breed"]}], "writes": [{"table": "company", "columns": ["connection", "breed"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT candidate_id, policy_name FROM ma_inspections LIMIT 227\")\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO industrial_customers SELECT crop_id, astronautid, date_of_publication, institution_id FROM vehicle_counts WHERE crop_id > 100\")\n", "labels": {"reads": [{"table": "ma_inspections", "columns": ["candidate_id", "policy_name"]}, {"table": "vehicle_counts", "columns": ["crop_id", "astronautid", "date_of_publication", "institution_id"]}], "writes": [{"table": "industrial_customers", "columns": ["crop_id", "astronautid", "date_of_publication", "institution_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"field4_precip\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "field4_precip", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO investment_strategies SELECT attendance, average FROM skincareinventory WHERE attendance > 324\"\n", "labels": {"reads": [{"table": "skincareinventory", "columns": ["attendance", "average"]}], "writes": [{"table": "investment_strategies", "columns": ["attendance", "average"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM violations\", conn)\ndf.to_sql(\"public.collected_fare\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "violations", "columns": null}], "writes": [{"table": "public.collected_fare", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"inclusion_efforts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"grapes\")\n", "labels": {"reads": [{"table": "inclusion_efforts", "columns": null}], "writes": [{"table": "grapes", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"mining_companies\")\nsrc.write.insertInto(\"strains\", overwrite=True)\n", "labels": {"reads": [{"table": "mining_companies", "columns": null}], "writes": [{"table": "strains", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM france_culture\"\n", "labels": {"reads": [{"table": "france_culture", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_sessions_delta\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_sessions_delta", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO tourism (venue_id, exhibitions) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "tourism", "columns": ["venue_id", "exhibitions"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM stg.campaigns_df\", conn)\ndf.to_sql(\"shelters\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "stg.campaigns_df", "columns": null}], "writes": [{"table": "shelters", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO aid_missions SELECT * FROM legacy\ncur.execute(\"SELECT route_id, incorporated_in FROM furniture LIMIT 294\")\n", "labels": {"reads": [{"table": "furniture", "columns": ["route_id", "incorporated_in"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO community.donors SELECT a.district_id, b.f_id FROM tour_guides a JOIN bi.bi_payments b ON a.discovered_date = b.discovered_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tour_guides", "columns": null}, {"table": "bi.bi_payments", "columns": null}], "writes": [{"table": "community.donors", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table community_policing_events --target-dir /tmp/land\n", "labels": {"reads": [{"table": "community_policing_events", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT incident_type_code, damage_millions_usd FROM travel_advisory LIMIT 412\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO county_public_safety SELECT credit_score, document_date, head_id FROM artistsales WHERE credit_score > 256\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": ["incident_type_code", "damage_millions_usd"]}, {"table": "artistsales", "columns": ["credit_score", "document_date", "head_id"]}], "writes": [{"table": "county_public_safety", "columns": ["credit_score", "document_date", "head_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO forests (center_name, petid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "forests", "columns": ["center_name", "petid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artprograms --columns post_id,pilot_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artprograms", "columns": ["post_id", "pilot_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM safety_incidents\", conn)\ndf.to_sql(\"claims\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "safety_incidents", "columns": null}], "writes": [{"table": "claims", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO player SELECT a.district_id, b.clicks FROM unions a JOIN materials_usage b ON a.developer = b.developer\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "unions", "columns": null}, {"table": "materials_usage", "columns": null}], "writes": [{"table": "player", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT delivery_date, trade_name FROM representative\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"investments_esg\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "representative", "columns": ["delivery_date", "trade_name"]}], "writes": [{"table": "investments_esg", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"management\")\npush_to_output(df, \"checking\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "management", "columns": null}], "writes": [{"table": "checking", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_input(ctx, \"green_buildings\")\nwrite_to_store(df, \"startup_founders\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "green_buildings", "columns": null}], "writes": [{"table": "startup_founders", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM timber_sales\"\n", "labels": {"reads": [{"table": "timber_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model player_coach depends on productivity\ndbt build --models player_coach --vars 'source: productivity'\n", "labels": {"reads": [{"table": "productivity", "columns": null}], "writes": [{"table": "player_coach", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climateresearch\"\n", "labels": {"reads": [{"table": "climateresearch", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT draft_class, produceid FROM socially_responsible_lending LIMIT 7\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "socially_responsible_lending", "columns": ["draft_class", "produceid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dispensaries\")\nsrc.write.insertInto(\"customers_policies\", overwrite=True)\n", "labels": {"reads": [{"table": "dispensaries", "columns": null}], "writes": [{"table": "customers_policies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM smartcities\"\n", "labels": {"reads": [{"table": "smartcities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table mine --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mine", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO artist_demographics SELECT generation_date, sales_billion, org_name, session_date FROM leo_missions WHERE generation_date > 181\"], check=True)\n", "labels": {"reads": [{"table": "leo_missions", "columns": ["generation_date", "sales_billion", "org_name", "session_date"]}], "writes": [{"table": "artist_demographics", "columns": ["generation_date", "sales_billion", "org_name", "session_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO manufacturersustainability SELECT team_id_br, request_date FROM residents_services WHERE team_id_br > 414;\nEOF\n", "labels": {"reads": [{"table": "residents_services", "columns": ["team_id_br", "request_date"]}], "writes": [{"table": "manufacturersustainability", "columns": ["team_id_br", "request_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO urban_agriculture_initiatives SELECT lot_id, organisation_details FROM caribbeansea WHERE lot_id > 408\"\n", "labels": {"reads": [{"table": "caribbeansea", "columns": ["lot_id", "organisation_details"]}], "writes": [{"table": "urban_agriculture_initiatives", "columns": ["lot_id", "organisation_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"match_result\").toPandas()\ndf[[\"hospitalname\", \"totalamount\"]].to_sql(\"vessel_capacity\", engine, index=False)\n", "labels": {"reads": [{"table": "match_result", "columns": null}], "writes": [{"table": "vessel_capacity", "columns": ["hospitalname", "totalamount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"culturalpractices\")\nsave_to_warehouse(df, \"school_roster\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "culturalpractices", "columns": null}], "writes": [{"table": "school_roster", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT device_id, gender_diversity FROM donors_region LIMIT 485\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "donors_region", "columns": ["device_id", "gender_diversity"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"financial_capability_programs\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "financial_capability_programs", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.case_number > 396).all()\n# src table: aquaculture_farms\nengine.execute(\"INSERT INTO prices SELECT * FROM aquaculture_farms\")\n", "labels": {"reads": [{"table": "aquaculture_farms", "columns": null}], "writes": [{"table": "prices", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"contract_negotiations_un\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model waterconservation depends on browser\ndbt build -s waterconservation --vars 'source: browser'\n", "labels": {"reads": [{"table": "browser", "columns": null}], "writes": [{"table": "waterconservation", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO influencers SELECT opponent_id, garment, s_id FROM affiliated_with WHERE opponent_id > 201;\nEOF\n", "labels": {"reads": [{"table": "affiliated_with", "columns": ["opponent_id", "garment", "s_id"]}], "writes": [{"table": "influencers", "columns": ["opponent_id", "garment", "s_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.date_complaint_raised > 323).all()\n# src table: electric_vehicle_stats\nengine.execute(\"INSERT INTO student_access SELECT * FROM electric_vehicle_stats\")\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": [{"table": "student_access", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table associatedheritages --target-dir /tmp/land\n", "labels": {"reads": [{"table": "associatedheritages", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM socialimpactinvestments\"\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model patient_satisfaction depends on climateresearch\ndbt build --models patient_satisfaction --vars '{\"src\":\"climateresearch\"}'\n", "labels": {"reads": [{"table": "climateresearch", "columns": null}], "writes": [{"table": "patient_satisfaction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthprovider\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mentalhealthprovider", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"country_labor\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"underwater_cables\")\n", "labels": {"reads": [{"table": "country_labor", "columns": null}], "writes": [{"table": "underwater_cables", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM residents_services\", conn)\ndf.to_sql(\"org_donation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "residents_services", "columns": null}], "writes": [{"table": "org_donation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO recruiters SELECT observation_date, pieces, time FROM opendatainitiatives WHERE observation_date > 108\"\n", "labels": {"reads": [{"table": "opendatainitiatives", "columns": ["observation_date", "pieces", "time"]}], "writes": [{"table": "recruiters", "columns": ["observation_date", "pieces", "time"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bank\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"sustainable_projects\")\n", "labels": {"reads": [{"table": "bank", "columns": null}], "writes": [{"table": "sustainable_projects", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"companies\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"traveler\")\n", "labels": {"reads": [{"table": "companies", "columns": null}], "writes": [{"table": "traveler", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO timber_sales SELECT salesperson_id, half, discovered_date, faculty_id FROM conditions WHERE salesperson_id > 465\")\n", "labels": {"reads": [{"table": "conditions", "columns": ["salesperson_id", "half", "discovered_date", "faculty_id"]}], "writes": [{"table": "timber_sales", "columns": ["salesperson_id", "half", "discovered_date", "faculty_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT involved_in_lifelong_learning, professional_development_programs FROM genetics.crispr LIMIT 173;\nINSERT INTO business_rates SELECT bridgetype, partnership_id, served_subscribers, watertemp FROM teacher_development_race WHERE bridgetype > 309;\nSQL\n", "labels": {"reads": [{"table": "genetics.crispr", "columns": ["involved_in_lifelong_learning", "professional_development_programs"]}, {"table": "teacher_development_race", "columns": ["bridgetype", "partnership_id", "served_subscribers", "watertemp"]}], "writes": [{"table": "business_rates", "columns": ["bridgetype", "partnership_id", "served_subscribers", "watertemp"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table machines --columns round,adoption_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "machines", "columns": ["round", "adoption_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model circuits depends on green_building_materials\ndbt run --select circuits --vars '{\"src\":\"green_building_materials\"}'\n", "labels": {"reads": [{"table": "green_building_materials", "columns": null}], "writes": [{"table": "circuits", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT implementation_year, pollution_id FROM cultivators LIMIT 185\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO water_treatment_facilities SELECT project_name, gamename, fairtrade FROM stock_levels WHERE project_name > 296\")\n", "labels": {"reads": [{"table": "cultivators", "columns": ["implementation_year", "pollution_id"]}, {"table": "stock_levels", "columns": ["project_name", "gamename", "fairtrade"]}], "writes": [{"table": "water_treatment_facilities", "columns": ["project_name", "gamename", "fairtrade"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO team_franchise SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO complaints SELECT * FROM legacy\ncur.execute(\"SELECT ad_id, avg_speed FROM smart_cities LIMIT 59\")\n", "labels": {"reads": [{"table": "smart_cities", "columns": ["ad_id", "avg_speed"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table safety_incidents_india --columns meal_name,payment_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "safety_incidents_india", "columns": ["meal_name", "payment_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT customername, max_aperture FROM singer_in_concert LIMIT 481;\nINSERT INTO ads.ads_users_hourly SELECT user_account, accident_date, investment_round, medical_condition FROM animal_rehab WHERE user_account > 188;\nSQL\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": ["customername", "max_aperture"]}, {"table": "animal_rehab", "columns": ["user_account", "accident_date", "investment_round", "medical_condition"]}], "writes": [{"table": "ads.ads_users_hourly", "columns": ["user_account", "accident_date", "investment_round", "medical_condition"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_risk_score_df\", conn)\ndf.to_sql(\"water_conservation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_risk_score_df", "columns": null}], "writes": [{"table": "water_conservation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO drug_sales SELECT date_formed, defense_contractor_id, wellname, totalprice FROM ticketspending WHERE date_formed > 75;\nEOF\n", "labels": {"reads": [{"table": "ticketspending", "columns": ["date_formed", "defense_contractor_id", "wellname", "totalprice"]}], "writes": [{"table": "drug_sales", "columns": ["date_formed", "defense_contractor_id", "wellname", "totalprice"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO traveler SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT call_date, price_in_dollar FROM space_telescopes LIMIT 26;\nINSERT INTO waste_generation SELECT involved_in_lifelong_learning, costid FROM defense_spending_3 WHERE involved_in_lifelong_learning > 401;\nSQL\n", "labels": {"reads": [{"table": "space_telescopes", "columns": ["call_date", "price_in_dollar"]}, {"table": "defense_spending_3", "columns": ["involved_in_lifelong_learning", "costid"]}], "writes": [{"table": "waste_generation", "columns": ["involved_in_lifelong_learning", "costid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO patients (director, programid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "patients", "columns": ["director", "programid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model online_platform depends on infrastructurebudget\ndbt run --select online_platform --vars 'source: infrastructurebudget'\n", "labels": {"reads": [{"table": "infrastructurebudget", "columns": null}], "writes": [{"table": "online_platform", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO virtual_tours SELECT a.success, b.prof_num FROM hotel_ratings a JOIN bi.bi_orders_hourly b ON a.enddate = b.enddate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "hotel_ratings", "columns": null}, {"table": "bi.bi_orders_hourly", "columns": null}], "writes": [{"table": "virtual_tours", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"labor_unions\")\nupsert_to_sink(df, \"job_postings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "labor_unions", "columns": null}], "writes": [{"table": "job_postings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT last_maintenance, q1_2022_views FROM uniteddefense.equipmentsales\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"policy_feedback\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "uniteddefense.equipmentsales", "columns": ["last_maintenance", "q1_2022_views"]}], "writes": [{"table": "policy_feedback", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO associatedheritages SELECT interest_group, customer_details FROM resilience_infrastructure WHERE interest_group > 477\"\n", "labels": {"reads": [{"table": "resilience_infrastructure", "columns": ["interest_group", "customer_details"]}], "writes": [{"table": "associatedheritages", "columns": ["interest_group", "customer_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO runs SELECT center, street_address, amount_settled, railway_id FROM canals WHERE center > 442\")\n", "labels": {"reads": [{"table": "canals", "columns": ["center", "street_address", "amount_settled", "railway_id"]}], "writes": [{"table": "runs", "columns": ["center", "street_address", "amount_settled", "railway_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model factories depends on urban_initiatives\ndbt run -s factories --vars '{\"source_table\":\"urban_initiatives\"}'\n", "labels": {"reads": [{"table": "urban_initiatives", "columns": null}], "writes": [{"table": "factories", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_table(ctx, \"ethicalaibudget\")\ndump_to_sink(df, \"transportation_per_country\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": [{"table": "transportation_per_country", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table airport_aircraft --columns enddate,vendor_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "airport_aircraft", "columns": ["enddate", "vendor_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO vehicle SELECT principal_activities, workerid, ocean_name, asset_name FROM clinics_sa WHERE principal_activities > 298\")\n", "labels": {"reads": [{"table": "clinics_sa", "columns": ["principal_activities", "workerid", "ocean_name", "asset_name"]}], "writes": [{"table": "vehicle", "columns": ["principal_activities", "workerid", "ocean_name", "asset_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO electric_vehicle_stats SELECT target_name, certification_id, port_id FROM food_justice_orgs WHERE target_name > 303\"], check=True)\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": ["target_name", "certification_id", "port_id"]}], "writes": [{"table": "electric_vehicle_stats", "columns": ["target_name", "certification_id", "port_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"multimodal_trips\")\nsrc.write.insertInto(\"suppliersfairlabor\", overwrite=True)\n", "labels": {"reads": [{"table": "multimodal_trips", "columns": null}], "writes": [{"table": "suppliersfairlabor", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO employee_demographics SELECT * FROM legacy\ncur.execute(\"SELECT paper_id, awayteamid FROM safety_incidents_india LIMIT 357\")\n", "labels": {"reads": [{"table": "safety_incidents_india", "columns": ["paper_id", "awayteamid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO southeast_providers SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_frame(ctx, \"intelligence_agents\")\nexport_to_output(df, \"hotel_tech_adoptions\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "intelligence_agents", "columns": null}], "writes": [{"table": "hotel_tech_adoptions", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO housing_investments SELECT individual_name, classid, airport_name FROM beauty_products WHERE individual_name > 460;\nEOF\n", "labels": {"reads": [{"table": "beauty_products", "columns": ["individual_name", "classid", "airport_name"]}], "writes": [{"table": "housing_investments", "columns": ["individual_name", "classid", "airport_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO buildings SELECT mountain_id, totaldonation, duration_ms FROM ods.sessions_daily WHERE mountain_id > 50\"\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": ["mountain_id", "totaldonation", "duration_ms"]}], "writes": [{"table": "buildings", "columns": ["mountain_id", "totaldonation", "duration_ms"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fish_biomass\").toPandas()\ndf[[\"scores\", \"farmid\"]].to_sql(\"country_sustainable_chains\", engine, index=False)\n", "labels": {"reads": [{"table": "fish_biomass", "columns": null}], "writes": [{"table": "country_sustainable_chains", "columns": ["scores", "farmid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ads_payments_di SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mobile_usage (dates_active, rec_engine) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mobile_usage", "columns": ["dates_active", "rec_engine"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"game_sessions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table fireincidents --columns contract_number,destruction_authorised_by_employee_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "fireincidents", "columns": ["contract_number", "destruction_authorised_by_employee_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.risk_score_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws.risk_score_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO crops SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model marketing_regions depends on renewable_projects\ndbt build -s marketing_regions --vars '{\"src\":\"renewable_projects\"}'\n", "labels": {"reads": [{"table": "renewable_projects", "columns": null}], "writes": [{"table": "marketing_regions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO renewableprojects (year_join, price_in_dollar) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "renewableprojects", "columns": ["year_join", "price_in_dollar"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ai_ethics_policies SELECT how_to_get_there, shariah_compliant_investment_amount FROM wastewatertreatment WHERE how_to_get_there > 39\"], check=True)\n", "labels": {"reads": [{"table": "wastewatertreatment", "columns": ["how_to_get_there", "shariah_compliant_investment_amount"]}], "writes": [{"table": "ai_ethics_policies", "columns": ["how_to_get_there", "shariah_compliant_investment_amount"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table educationprograms --columns hotel_name,labordate --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "educationprograms", "columns": ["hotel_name", "labordate"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM movie_financials\"\n", "labels": {"reads": [{"table": "movie_financials", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd.dwd_campaigns\", conn)\ndf.to_sql(\"neodymium_prices\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns", "columns": null}], "writes": [{"table": "neodymium_prices", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO apac_hotel_views SELECT park_id, certification_name FROM labor_stats WHERE park_id > 409\"\n", "labels": {"reads": [{"table": "labor_stats", "columns": ["park_id", "certification_name"]}], "writes": [{"table": "apac_hotel_views", "columns": ["park_id", "certification_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"smart_city_projects\")\nsrc.write.insertInto(\"hydro_plants\", overwrite=True)\n", "labels": {"reads": [{"table": "smart_city_projects", "columns": null}], "writes": [{"table": "hydro_plants", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"highest_scores\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "highest_scores", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"mobile_customers_global\")\nupsert_to_warehouse(df, \"habitat_preservation\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mobile_customers_global", "columns": null}], "writes": [{"table": "habitat_preservation", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model visits_restaurant depends on dali\ndbt run -s visits_restaurant --vars '{\"source_table\":\"dali\"}'\n", "labels": {"reads": [{"table": "dali", "columns": null}], "writes": [{"table": "visits_restaurant", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO investmentsesg (trainingtitle, lifespan) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "investmentsesg", "columns": ["trainingtitle", "lifespan"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table traffic_accidents --target-dir /tmp/land\n", "labels": {"reads": [{"table": "traffic_accidents", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT price, nid FROM customers\", engine)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"articles\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customers", "columns": ["price", "nid"]}], "writes": [{"table": "articles", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.dws_coupon_use_full SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO marathons (playerid, strain_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "marathons", "columns": ["playerid", "strain_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.contract_type > 117).all()\n# src table: mentalhealthparityscores\nengine.execute(\"INSERT INTO restaurant_revenue SELECT * FROM mentalhealthparityscores\")\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": [{"table": "restaurant_revenue", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workforce_development\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ai_ethics_policies\")\n", "labels": {"reads": [{"table": "workforce_development", "columns": null}], "writes": [{"table": "ai_ethics_policies", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fleet_management SELECT is_electric, reported_by_staff_id, total_amount, transaction_date FROM climate_investments WHERE is_electric > 6\"\n", "labels": {"reads": [{"table": "climate_investments", "columns": ["is_electric", "reported_by_staff_id", "total_amount", "transaction_date"]}], "writes": [{"table": "fleet_management", "columns": ["is_electric", "reported_by_staff_id", "total_amount", "transaction_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artcontributors\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"jobs\")\n", "labels": {"reads": [{"table": "artcontributors", "columns": null}], "writes": [{"table": "jobs", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table skincareinventory --columns totaldonation,cultivatorname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "skincareinventory", "columns": ["totaldonation", "cultivatorname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dysprosiumproduction SELECT crs_description, used_kb, aircraft, assessmentname FROM undergoes WHERE crs_description > 340\"], check=True)\n", "labels": {"reads": [{"table": "undergoes", "columns": ["crs_description", "used_kb", "aircraft", "assessmentname"]}], "writes": [{"table": "dysprosiumproduction", "columns": ["crs_description", "used_kb", "aircraft", "assessmentname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fish_population, sustainability_certified FROM esports_teams LIMIT 133\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "esports_teams", "columns": ["fish_population", "sustainability_certified"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT trainingyear, cvid FROM healthcare_access_v2 LIMIT 366;\nINSERT INTO convictions SELECT channel_code, artifact_id FROM safetytestingcounts WHERE channel_code > 118;\nSQL\n", "labels": {"reads": [{"table": "healthcare_access_v2", "columns": ["trainingyear", "cvid"]}, {"table": "safetytestingcounts", "columns": ["channel_code", "artifact_id"]}], "writes": [{"table": "convictions", "columns": ["channel_code", "artifact_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table program_history --target-dir /tmp/land\n", "labels": {"reads": [{"table": "program_history", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vocals SELECT brand_name, org_size FROM productsafety WHERE brand_name > 492\"], check=True)\n", "labels": {"reads": [{"table": "productsafety", "columns": ["brand_name", "org_size"]}], "writes": [{"table": "vocals", "columns": ["brand_name", "org_size"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mart.clicks SELECT movieid, bikes_available FROM location WHERE movieid > 359\"\n", "labels": {"reads": [{"table": "location", "columns": ["movieid", "bikes_available"]}], "writes": [{"table": "mart.clicks", "columns": ["movieid", "bikes_available"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.stg_risk_score_hourly (call_date, created_at) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_risk_score_hourly", "columns": ["call_date", "created_at"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table aircraftsquadrons --columns hispanic,program_type --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "aircraftsquadrons", "columns": ["hispanic", "program_type"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT region_id, change_date FROM europium_exports LIMIT 307;\nINSERT INTO patents SELECT fund_id, partner FROM product_review WHERE fund_id > 429;\nSQL\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["region_id", "change_date"]}, {"table": "product_review", "columns": ["fund_id", "partner"]}], "writes": [{"table": "patents", "columns": ["fund_id", "partner"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO membership_data SELECT a.sent_date, b.park_id FROM attendees a JOIN trees b ON a.facility_id = b.facility_id\"\n", "labels": {"reads": [{"table": "attendees", "columns": null}, {"table": "trees", "columns": null}], "writes": [{"table": "membership_data", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"justice_schemas.legal_tech_providers\")\nsrc.write.insertInto(\"eco_materials\", overwrite=True)\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": null}], "writes": [{"table": "eco_materials", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods.ods_member_point_df\")\nsrc.write.insertInto(\"awards\", overwrite=True)\n", "labels": {"reads": [{"table": "ods.ods_member_point_df", "columns": null}], "writes": [{"table": "awards", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO singer SELECT swimmer_id, fair_trade, funding_year FROM recyclingprogram WHERE swimmer_id > 220\")\n", "labels": {"reads": [{"table": "recyclingprogram", "columns": ["swimmer_id", "fair_trade", "funding_year"]}], "writes": [{"table": "singer", "columns": ["swimmer_id", "fair_trade", "funding_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO design_standards SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO biosensors.patents (accessibility, f_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "biosensors.patents", "columns": ["accessibility", "f_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM fare_segments\"\n", "labels": {"reads": [{"table": "fare_segments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM union_membership\"\n", "labels": {"reads": [{"table": "union_membership", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"product_review\")\nsrc.write.insertInto(\"union_members\", overwrite=True)\n", "labels": {"reads": [{"table": "product_review", "columns": null}], "writes": [{"table": "union_members", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT material_type, mgr_start_date FROM shared_rides_tokyo LIMIT 26\")\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO document_locations SELECT num_volunteers, implementation_date, booking_start_date, city_population FROM songs WHERE num_volunteers > 94\")\n", "labels": {"reads": [{"table": "shared_rides_tokyo", "columns": ["material_type", "mgr_start_date"]}, {"table": "songs", "columns": ["num_volunteers", "implementation_date", "booking_start_date", "city_population"]}], "writes": [{"table": "document_locations", "columns": ["num_volunteers", "implementation_date", "booking_start_date", "city_population"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO chemical_processes SELECT appelation, wage, analysis_date FROM time_dim WHERE appelation > 421\"\n", "labels": {"reads": [{"table": "time_dim", "columns": ["appelation", "wage", "analysis_date"]}], "writes": [{"table": "chemical_processes", "columns": ["appelation", "wage", "analysis_date"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM philadelphia_police_emergencies\"\n", "labels": {"reads": [{"table": "philadelphia_police_emergencies", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO salary SELECT booking_start_date, genre, product FROM facility_production WHERE booking_start_date > 147\"\n", "labels": {"reads": [{"table": "facility_production", "columns": ["booking_start_date", "genre", "product"]}], "writes": [{"table": "salary", "columns": ["booking_start_date", "genre", "product"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO labor_statistics SELECT apid, tour_id, product_size FROM disaster_response WHERE apid > 252\"\n", "labels": {"reads": [{"table": "disaster_response", "columns": ["apid", "tour_id", "product_size"]}], "writes": [{"table": "labor_statistics", "columns": ["apid", "tour_id", "product_size"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT audienceid, program FROM camera_lens\", engine)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"associatedheritages\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "camera_lens", "columns": ["audienceid", "program"]}], "writes": [{"table": "associatedheritages", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"new_schedules\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "new_schedules", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO public.collected_fare SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM team_members\", conn)\ndf.to_sql(\"accelerator_compatible_browser\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "team_members", "columns": null}], "writes": [{"table": "accelerator_compatible_browser", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO certificate SELECT 1\"\ntrap 'echo failed' ERR\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"infantmortalitydata\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"healthcare_access_v2\")\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": null}], "writes": [{"table": "healthcare_access_v2", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM italy_culture\"\n", "labels": {"reads": [{"table": "italy_culture", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM resource_extraction\"\n", "labels": {"reads": [{"table": "resource_extraction", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT water_temp, mgr_start_date FROM patient_outcomes LIMIT 78\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": ["water_temp", "mgr_start_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table checking --columns promotionid,gender_mf --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "checking", "columns": ["promotionid", "gender_mf"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"acidification_data\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "acidification_data", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT type, donationdate FROM infrastructure_projects LIMIT 44\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "infrastructure_projects", "columns": ["type", "donationdate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO intelligencesatellites SELECT num_students, maintenanceid, medium FROM green_buildings WHERE num_students > 317\"\n", "labels": {"reads": [{"table": "green_buildings", "columns": ["num_students", "maintenanceid", "medium"]}], "writes": [{"table": "intelligencesatellites", "columns": ["num_students", "maintenanceid", "medium"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT cultural_diversity, booking_id FROM race_ethnicity LIMIT 488;\nINSERT INTO community_centers SELECT university_type, task_details, max_cargo_weight, host_city_id FROM emergencyservices WHERE university_type > 324;\nSQL\n", "labels": {"reads": [{"table": "race_ethnicity", "columns": ["cultural_diversity", "booking_id"]}, {"table": "emergencyservices", "columns": ["university_type", "task_details", "max_cargo_weight", "host_city_id"]}], "writes": [{"table": "community_centers", "columns": ["university_type", "task_details", "max_cargo_weight", "host_city_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mine_name, customer_details FROM railway LIMIT 63\")\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO block SELECT crime_date, enrollment FROM bi.bi_sessions_df WHERE crime_date > 284\")\n", "labels": {"reads": [{"table": "railway", "columns": ["mine_name", "customer_details"]}, {"table": "bi.bi_sessions_df", "columns": ["crime_date", "enrollment"]}], "writes": [{"table": "block", "columns": ["crime_date", "enrollment"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO clinics_sa SELECT channel_code, log_entry_description, employee_id FROM sustainable_materials WHERE channel_code > 406\"\n", "labels": {"reads": [{"table": "sustainable_materials", "columns": ["channel_code", "log_entry_description", "employee_id"]}], "writes": [{"table": "clinics_sa", "columns": ["channel_code", "log_entry_description", "employee_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT state_county, communitytype FROM runs LIMIT 132;\nINSERT INTO dws.dws_coupon_use_di SELECT contract_value, concert_id, incidentdate FROM zip_codes WHERE contract_value > 322;\nSQL\n", "labels": {"reads": [{"table": "runs", "columns": ["state_county", "communitytype"]}, {"table": "zip_codes", "columns": ["contract_value", "concert_id", "incidentdate"]}], "writes": [{"table": "dws.dws_coupon_use_di", "columns": ["contract_value", "concert_id", "incidentdate"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO jupiter_spacecraft SELECT a.mission, b.topic FROM submission a JOIN salesdata b ON a.state_name = b.state_name\"\n", "labels": {"reads": [{"table": "submission", "columns": null}, {"table": "salesdata", "columns": null}], "writes": [{"table": "jupiter_spacecraft", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table esa_missions --columns store_name,num_attendees --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "esa_missions", "columns": ["store_name", "num_attendees"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO housingaffordability SELECT document_date, game_count FROM gameattendance WHERE document_date > 183\"\n", "labels": {"reads": [{"table": "gameattendance", "columns": ["document_date", "game_count"]}], "writes": [{"table": "housingaffordability", "columns": ["document_date", "game_count"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO vrgames SELECT case_status, claim_outcome_code, other_characteristic_details FROM student_program_mapping WHERE case_status > 493;\nEOF\n", "labels": {"reads": [{"table": "student_program_mapping", "columns": ["case_status", "claim_outcome_code", "other_characteristic_details"]}], "writes": [{"table": "vrgames", "columns": ["case_status", "claim_outcome_code", "other_characteristic_details"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"conservation_projects\")\nsrc.write.insertInto(\"train_lines\", overwrite=True)\n", "labels": {"reads": [{"table": "conservation_projects", "columns": null}], "writes": [{"table": "train_lines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"developers\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mental_health_parity_violations\")\n", "labels": {"reads": [{"table": "developers", "columns": null}], "writes": [{"table": "mental_health_parity_violations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO appointment (stream_date, follow_up_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "appointment", "columns": ["stream_date", "follow_up_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO military_equipment_maintenance SELECT active_from_date, energy_source FROM episodes WHERE active_from_date > 301\"], check=True)\n", "labels": {"reads": [{"table": "episodes", "columns": ["active_from_date", "energy_source"]}], "writes": [{"table": "military_equipment_maintenance", "columns": ["active_from_date", "energy_source"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.dws_events_df (donorname, state_province) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_events_df", "columns": ["donorname", "state_province"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO film_category SELECT retailer_id, loadingstart, player_name, cows FROM ai_papers WHERE retailer_id > 470\"\n", "labels": {"reads": [{"table": "ai_papers", "columns": ["retailer_id", "loadingstart", "player_name", "cows"]}], "writes": [{"table": "film_category", "columns": ["retailer_id", "loadingstart", "player_name", "cows"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model canals depends on mart.mart_refunds_di\ndbt run --models canals --vars '{\"src\":\"mart.mart_refunds_di\"}'\n", "labels": {"reads": [{"table": "mart.mart_refunds_di", "columns": null}], "writes": [{"table": "canals", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM inspections\"\n", "labels": {"reads": [{"table": "inspections", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO indie_artists SELECT cell_mobile_number, allocation_type FROM stg.device_log_df WHERE cell_mobile_number > 333;\nEOF\n", "labels": {"reads": [{"table": "stg.device_log_df", "columns": ["cell_mobile_number", "allocation_type"]}], "writes": [{"table": "indie_artists", "columns": ["cell_mobile_number", "allocation_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart_refunds_delta --columns casestatus,continent --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart_refunds_delta", "columns": ["casestatus", "continent"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model garmentproduction depends on dwd.users_daily\ndbt run --select garmentproduction --vars '{\"src\":\"dwd.users_daily\"}'\n", "labels": {"reads": [{"table": "dwd.users_daily", "columns": null}], "writes": [{"table": "garmentproduction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM drought_data\"\n", "labels": {"reads": [{"table": "drought_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM equipment_sales\"\n", "labels": {"reads": [{"table": "equipment_sales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO budgets SELECT claimamount, cityname, sighting_id FROM vehicle_sales WHERE claimamount > 464\"\n", "labels": {"reads": [{"table": "vehicle_sales", "columns": ["claimamount", "cityname", "sighting_id"]}], "writes": [{"table": "budgets", "columns": ["claimamount", "cityname", "sighting_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"mappinglengths\")\nupsert_to_store(df, \"inmates\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mappinglengths", "columns": null}], "writes": [{"table": "inmates", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO animals SELECT a.vegan, b.community FROM game_sales a JOIN arcticwildlifereserve b ON a.review_id = b.review_id\"\n", "labels": {"reads": [{"table": "game_sales", "columns": null}, {"table": "arcticwildlifereserve", "columns": null}], "writes": [{"table": "animals", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mine_workforce (characteristic_name, coach_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mine_workforce", "columns": ["characteristic_name", "coach_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_frame(ctx, \"bi.member_point_full\")\nsink_to_output(df, \"wastewater_treatment_plants\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": null}], "writes": [{"table": "wastewater_treatment_plants", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO ods_member_point_full SELECT a.asset_details, b.detention_type_code FROM therapy_attendance a JOIN mart.mart_coupon_use_full b ON a.campaign_name = b.campaign_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "therapy_attendance", "columns": null}, {"table": "mart.mart_coupon_use_full", "columns": null}], "writes": [{"table": "ods_member_point_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT cultivatorid, sessiondate FROM ads.ads_member_point_daily LIMIT 351\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO baseball_teams SELECT unit_of_measure, review_rating, is_vegan, volunteer_hours FROM laborstatistics WHERE unit_of_measure > 19\")\n", "labels": {"reads": [{"table": "ads.ads_member_point_daily", "columns": ["cultivatorid", "sessiondate"]}, {"table": "laborstatistics", "columns": ["unit_of_measure", "review_rating", "is_vegan", "volunteer_hours"]}], "writes": [{"table": "baseball_teams", "columns": ["unit_of_measure", "review_rating", "is_vegan", "volunteer_hours"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO marine_species_status SELECT * FROM legacy\ncur.execute(\"SELECT centerid, vesselid FROM dws.dws_clicks_full LIMIT 192\")\n", "labels": {"reads": [{"table": "dws.dws_clicks_full", "columns": ["centerid", "vesselid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT cinema_id, size FROM ads.refunds\", engine)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"bustrips\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.refunds", "columns": ["cinema_id", "size"]}], "writes": [{"table": "bustrips", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.cart_item_di SELECT * FROM legacy\ncur.execute(\"SELECT defense_contractor_id, request_date FROM matches LIMIT 137\")\n", "labels": {"reads": [{"table": "matches", "columns": ["defense_contractor_id", "request_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO city_budgets SELECT shoe_brand, online_dispute_resolution FROM attorneylocationyear WHERE shoe_brand > 52;\nEOF\n", "labels": {"reads": [{"table": "attorneylocationyear", "columns": ["shoe_brand", "online_dispute_resolution"]}], "writes": [{"table": "city_budgets", "columns": ["shoe_brand", "online_dispute_resolution"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO races SELECT a.equipment_id, b.patentexpirationdate FROM water_sources a JOIN co_ownership b ON a.made_in_usa = b.made_in_usa\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "water_sources", "columns": null}, {"table": "co_ownership", "columns": null}], "writes": [{"table": "races", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"material\")\nsrc.write.insertInto(\"contractorsales\", overwrite=True)\n", "labels": {"reads": [{"table": "material", "columns": null}], "writes": [{"table": "contractorsales", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO refugee_support SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model authenticationlogs depends on biotech.startups\ndbt run --models authenticationlogs --vars '{\"source_table\":\"biotech.startups\"}'\n", "labels": {"reads": [{"table": "biotech.startups", "columns": null}], "writes": [{"table": "authenticationlogs", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO submersible_dives SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.coupon_use\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"languagesatrisk\")\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": null}], "writes": [{"table": "languagesatrisk", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table solar_energy --target-dir /tmp/land\n", "labels": {"reads": [{"table": "solar_energy", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO airport_aircraft SELECT postal_code, store_name FROM market_trends WHERE postal_code > 183\"\n", "labels": {"reads": [{"table": "market_trends", "columns": ["postal_code", "store_name"]}], "writes": [{"table": "airport_aircraft", "columns": ["postal_code", "store_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM upgrades\", conn)\ndf.to_sql(\"professional_development\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "upgrades", "columns": null}], "writes": [{"table": "professional_development", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO carbon_sequestration SELECT a.fundingamount, b.cost FROM wind_turbines a JOIN restaurants b ON a.measurement_id = b.measurement_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "wind_turbines", "columns": null}, {"table": "restaurants", "columns": null}], "writes": [{"table": "carbon_sequestration", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM gamestats\"\n", "labels": {"reads": [{"table": "gamestats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM events\"\n", "labels": {"reads": [{"table": "events", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_frame(ctx, \"auto_shows\")\ndump_to_target(df, \"satellitedata\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "auto_shows", "columns": null}], "writes": [{"table": "satellitedata", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT effort, institution FROM volunteers LIMIT 384\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "volunteers", "columns": ["effort", "institution"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bioreactor SELECT share_count, athlete_name FROM driver WHERE share_count > 271\"\n", "labels": {"reads": [{"table": "driver", "columns": ["share_count", "athlete_name"]}], "writes": [{"table": "bioreactor", "columns": ["share_count", "athlete_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO teacher_professional_development SELECT a.theme, b.stop FROM carbon_offset_programs a JOIN budget b ON a.date_of_enrolment = b.date_of_enrolment\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "carbon_offset_programs", "columns": null}, {"table": "budget", "columns": null}], "writes": [{"table": "teacher_professional_development", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dws.dws_users_hourly SELECT employer_organisation_id, green_building_id FROM satisfaction WHERE employer_organisation_id > 291\"\n", "labels": {"reads": [{"table": "satisfaction", "columns": ["employer_organisation_id", "green_building_id"]}], "writes": [{"table": "dws.dws_users_hourly", "columns": ["employer_organisation_id", "green_building_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workout\"\n", "labels": {"reads": [{"table": "workout", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"county\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"crops\")\n", "labels": {"reads": [{"table": "county", "columns": null}], "writes": [{"table": "crops", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO workforce SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table sourcing --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sourcing", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO sector_incidents SELECT a.contributorid, b.policy_id FROM taj_mahal_visitors a JOIN pipelines_us_canada b ON a.participant_type_code = b.participant_type_code\"\n", "labels": {"reads": [{"table": "taj_mahal_visitors", "columns": null}, {"table": "pipelines_us_canada", "columns": null}], "writes": [{"table": "sector_incidents", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agencies SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"military_tech\")\ndump_to_target(df, \"stg.risk_score_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "military_tech", "columns": null}], "writes": [{"table": "stg.risk_score_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO experience SELECT apid, num_of_audience, recruitername FROM military_innovation WHERE apid > 497\"\n", "labels": {"reads": [{"table": "military_innovation", "columns": ["apid", "num_of_audience", "recruitername"]}], "writes": [{"table": "experience", "columns": ["apid", "num_of_audience", "recruitername"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT athleteid, official_native_language FROM government.city LIMIT 303;\nINSERT INTO habitats SELECT artist_id, subscriber_id, eventattendance FROM judges WHERE artist_id > 172;\nSQL\n", "labels": {"reads": [{"table": "government.city", "columns": ["athleteid", "official_native_language"]}, {"table": "judges", "columns": ["artist_id", "subscriber_id", "eventattendance"]}], "writes": [{"table": "habitats", "columns": ["artist_id", "subscriber_id", "eventattendance"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model job_postings depends on container_receipts\ndbt build --models job_postings --vars '{\"source_table\":\"container_receipts\"}'\n", "labels": {"reads": [{"table": "container_receipts", "columns": null}], "writes": [{"table": "job_postings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO music_festival SELECT strategy_name, field_id FROM dws.dws_clicks_full WHERE strategy_name > 68\")\n", "labels": {"reads": [{"table": "dws.dws_clicks_full", "columns": ["strategy_name", "field_id"]}], "writes": [{"table": "music_festival", "columns": ["strategy_name", "field_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO carbon_offset_initiatives SELECT characteristic_data_type, startdate, review_score FROM mars_rovers WHERE characteristic_data_type > 15\"\n", "labels": {"reads": [{"table": "mars_rovers", "columns": ["characteristic_data_type", "startdate", "review_score"]}], "writes": [{"table": "carbon_offset_initiatives", "columns": ["characteristic_data_type", "startdate", "review_score"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO dwd.dwd_payments_di SELECT industry_4_0, start_time, investors, amount_waste FROM vessel_incident_count WHERE industry_4_0 > 258\")\n", "labels": {"reads": [{"table": "vessel_incident_count", "columns": ["industry_4_0", "start_time", "investors", "amount_waste"]}], "writes": [{"table": "dwd.dwd_payments_di", "columns": ["industry_4_0", "start_time", "investors", "amount_waste"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.isfirstattendee > 112).all()\n# src table: fish_suppliers\nengine.execute(\"INSERT INTO incidents SELECT * FROM fish_suppliers\")\n", "labels": {"reads": [{"table": "fish_suppliers", "columns": null}], "writes": [{"table": "incidents", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO communitycenters SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tourism_centers (degrees, date_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tourism_centers", "columns": ["degrees", "date_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO genetic.projects SELECT away_team_id, algorithm_name, title, artworkid FROM ods.ods_campaigns_delta WHERE away_team_id > 88\"\n", "labels": {"reads": [{"table": "ods.ods_campaigns_delta", "columns": ["away_team_id", "algorithm_name", "title", "artworkid"]}], "writes": [{"table": "genetic.projects", "columns": ["away_team_id", "algorithm_name", "title", "artworkid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.ads_payments\", conn)\ndf.to_sql(\"soilmoisturedata\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_payments", "columns": null}], "writes": [{"table": "soilmoisturedata", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.policy > 91).all()\n# src table: forest_species\nengine.execute(\"INSERT INTO station_company SELECT * FROM forest_species\")\n", "labels": {"reads": [{"table": "forest_species", "columns": null}], "writes": [{"table": "station_company", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT community_center_id, breed FROM humanitarian_aid LIMIT 197\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "humanitarian_aid", "columns": ["community_center_id", "breed"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO aid_missions SELECT caloric_content, dispensary_id, projecttype FROM explainable_ai WHERE caloric_content > 364\"\n", "labels": {"reads": [{"table": "explainable_ai", "columns": ["caloric_content", "dispensary_id", "projecttype"]}], "writes": [{"table": "aid_missions", "columns": ["caloric_content", "dispensary_id", "projecttype"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO algorithmic_fairness_incidents SELECT a.programtype, b.circular_supply_chain FROM country_labor a JOIN stations b ON a.facility_code = b.facility_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "country_labor", "columns": null}, {"table": "stations", "columns": null}], "writes": [{"table": "algorithmic_fairness_incidents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO ods.products_hourly SELECT safety_score, biome_id, waste_type, programarea FROM products WHERE safety_score > 74\")\n", "labels": {"reads": [{"table": "products", "columns": ["safety_score", "biome_id", "waste_type", "programarea"]}], "writes": [{"table": "ods.products_hourly", "columns": ["safety_score", "biome_id", "waste_type", "programarea"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cb_agreements SELECT method_name, detention_type_description, pricepergram FROM developers WHERE method_name > 407\"\n", "labels": {"reads": [{"table": "developers", "columns": ["method_name", "detention_type_description", "pricepergram"]}], "writes": [{"table": "cb_agreements", "columns": ["method_name", "detention_type_description", "pricepergram"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ads.refunds_delta --columns gold,end_time --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ads.refunds_delta", "columns": ["gold", "end_time"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO royal_family SELECT official_native_language, altitude, visitid FROM fireincidents WHERE official_native_language > 129\"\n", "labels": {"reads": [{"table": "fireincidents", "columns": ["official_native_language", "altitude", "visitid"]}], "writes": [{"table": "royal_family", "columns": ["official_native_language", "altitude", "visitid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"workforce_development_programs\").toPandas()\ndf[[\"orgid\", \"asset_type\"]].to_sql(\"bus_fare_collection\", engine, index=False)\n", "labels": {"reads": [{"table": "workforce_development_programs", "columns": null}], "writes": [{"table": "bus_fare_collection", "columns": ["orgid", "asset_type"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO candidates SELECT usage, project, vendorid FROM food_safety_inspections WHERE usage > 210\")\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": ["usage", "project", "vendorid"]}], "writes": [{"table": "candidates", "columns": ["usage", "project", "vendorid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT union_id, cust_name FROM transportation_union LIMIT 439\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "transportation_union", "columns": ["union_id", "cust_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO school_bus SELECT violation_type, time_hour, reported_by_staff_id, customer_type_code FROM ocean_acidification_antarctic WHERE violation_type > 42\"\n", "labels": {"reads": [{"table": "ocean_acidification_antarctic", "columns": ["violation_type", "time_hour", "reported_by_staff_id", "customer_type_code"]}], "writes": [{"table": "school_bus", "columns": ["violation_type", "time_hour", "reported_by_staff_id", "customer_type_code"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_species\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fish_suppliers\")\n", "labels": {"reads": [{"table": "ocean_species", "columns": null}], "writes": [{"table": "fish_suppliers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mentalhealthscores SELECT artworkname, game FROM sustainability WHERE artworkname > 463\"\n", "labels": {"reads": [{"table": "sustainability", "columns": ["artworkname", "game"]}], "writes": [{"table": "mentalhealthscores", "columns": ["artworkname", "game"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.gameid > 383).all()\n# src table: elimination\nengine.execute(\"INSERT INTO plays_games SELECT * FROM elimination\")\n", "labels": {"reads": [{"table": "elimination", "columns": null}], "writes": [{"table": "plays_games", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO geological_survey SELECT last_year, carrierid FROM news_views WHERE last_year > 393\"\n", "labels": {"reads": [{"table": "news_views", "columns": ["last_year", "carrierid"]}], "writes": [{"table": "geological_survey", "columns": ["last_year", "carrierid"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO weather SELECT health_equity_metric_2, delivery_date, county_name FROM accelerator_compatible_browser WHERE health_equity_metric_2 > 358\"\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": ["health_equity_metric_2", "delivery_date", "county_name"]}], "writes": [{"table": "weather", "columns": ["health_equity_metric_2", "delivery_date", "county_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO garmentproduction SELECT a.vehicle_flight_number, b.astronaut FROM stats a JOIN athlete_wellbeing b ON a.inventor_name = b.inventor_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "stats", "columns": null}, {"table": "athlete_wellbeing", "columns": null}], "writes": [{"table": "garmentproduction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT budget, completion_status FROM spending LIMIT 45\")\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO fishcaught SELECT donortype, production_volume, artworkyear, party_name FROM otas WHERE donortype > 44\")\n", "labels": {"reads": [{"table": "spending", "columns": ["budget", "completion_status"]}, {"table": "otas", "columns": ["donortype", "production_volume", "artworkyear", "party_name"]}], "writes": [{"table": "fishcaught", "columns": ["donortype", "production_volume", "artworkyear", "party_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO brandrevenue SELECT flight_number, complaint_type_code, route_short_name FROM ads_payments_di WHERE flight_number > 312;\nEOF\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": ["flight_number", "complaint_type_code", "route_short_name"]}], "writes": [{"table": "brandrevenue", "columns": ["flight_number", "complaint_type_code", "route_short_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT budgeted, business_size FROM party_host LIMIT 163\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "party_host", "columns": ["budgeted", "business_size"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table store --target-dir /tmp/land\n", "labels": {"reads": [{"table": "store", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_frame(ctx, \"workercontactinfo\")\nexport_to_warehouse(df, \"ref_budget_codes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "workercontactinfo", "columns": null}], "writes": [{"table": "ref_budget_codes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO species_data SELECT item, item_name, home_city FROM az_drought_impact WHERE item > 4;\nEOF\n", "labels": {"reads": [{"table": "az_drought_impact", "columns": ["item", "item_name", "home_city"]}], "writes": [{"table": "species_data", "columns": ["item", "item_name", "home_city"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_payments SELECT transact_date, i_id, publication_id FROM funding_records WHERE transact_date > 35\"\n", "labels": {"reads": [{"table": "funding_records", "columns": ["transact_date", "i_id", "publication_id"]}], "writes": [{"table": "ads.ads_payments", "columns": ["transact_date", "i_id", "publication_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT suppliername, account_id FROM arctictemperature LIMIT 235\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ai_papers SELECT ethnicity, session_date FROM programoutcomes WHERE ethnicity > 9\")\n", "labels": {"reads": [{"table": "arctictemperature", "columns": ["suppliername", "account_id"]}, {"table": "programoutcomes", "columns": ["ethnicity", "session_date"]}], "writes": [{"table": "ai_papers", "columns": ["ethnicity", "session_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"traffic_accidents\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "traffic_accidents", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO item SELECT menu_item, activity_id FROM military_tech WHERE menu_item > 157\"], check=True)\n", "labels": {"reads": [{"table": "military_tech", "columns": ["menu_item", "activity_id"]}], "writes": [{"table": "item", "columns": ["menu_item", "activity_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT nickname, drug_name FROM playersessions LIMIT 50\")\nrows = cur.fetchall()\nimport logging\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "playersessions", "columns": ["nickname", "drug_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi.bi_events_full\"\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.expertise > 470).all()\n# src table: eco_diversification_investment\nengine.execute(\"INSERT INTO military_aircraft_maintenance SELECT * FROM eco_diversification_investment\")\n", "labels": {"reads": [{"table": "eco_diversification_investment", "columns": null}], "writes": [{"table": "military_aircraft_maintenance", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.profession > 124).all()\n# src table: foodaid\nengine.execute(\"INSERT INTO device_usage SELECT * FROM foodaid\")\n", "labels": {"reads": [{"table": "foodaid", "columns": null}], "writes": [{"table": "device_usage", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dates\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"customer_events\")\n", "labels": {"reads": [{"table": "dates", "columns": null}], "writes": [{"table": "customer_events", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"customer_payments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "customer_payments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.diet > 432).all()\n# src table: travel_advisory\nengine.execute(\"INSERT INTO ods.ods_member_point_delta SELECT * FROM travel_advisory\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "ods.ods_member_point_delta", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO disease_prevalence (granteeid, recordid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "disease_prevalence", "columns": ["granteeid", "recordid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.speed > 287).all()\n# src table: head\nengine.execute(\"INSERT INTO contract_negotiations SELECT * FROM head\")\n", "labels": {"reads": [{"table": "head", "columns": null}], "writes": [{"table": "contract_negotiations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO manufacturing_processes SELECT well_depth, thing_id, num_developments FROM cases WHERE well_depth > 277\"\n", "labels": {"reads": [{"table": "cases", "columns": ["well_depth", "thing_id", "num_developments"]}], "writes": [{"table": "manufacturing_processes", "columns": ["well_depth", "thing_id", "num_developments"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO lead_mines SELECT a.dst_apid, b.status_of_thing_code FROM social_impact_bonds a JOIN production b ON a.role_code = b.role_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "social_impact_bonds", "columns": null}, {"table": "production", "columns": null}], "writes": [{"table": "lead_mines", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO medical_facilities_nyc SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO deep_sea_species SELECT * FROM legacy\ncur.execute(\"SELECT eco_certified, state_code FROM runs LIMIT 170\")\n", "labels": {"reads": [{"table": "runs", "columns": ["eco_certified", "state_code"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"ods.clicks_delta\")\nupsert_to_sink(df, \"intelligenceoperations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods.clicks_delta", "columns": null}], "writes": [{"table": "intelligenceoperations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO exam_results SELECT safety_id, purchase_date, trial_name, cvid FROM dwd.coupon_use_full WHERE safety_id > 317;\nEOF\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["safety_id", "purchase_date", "trial_name", "cvid"]}], "writes": [{"table": "exam_results", "columns": ["safety_id", "purchase_date", "trial_name", "cvid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_member_point_di\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"student_tests_taken\")\n", "labels": {"reads": [{"table": "dw.dw_member_point_di", "columns": null}], "writes": [{"table": "student_tests_taken", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dorm_name, is_electric FROM algorithmic_fairness_incidents\", engine)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"player_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "algorithmic_fairness_incidents", "columns": ["dorm_name", "is_electric"]}], "writes": [{"table": "player_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vr_tech SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO daily_revenue SELECT investor, museum_id FROM rare_earth_companies WHERE investor > 48\"], check=True)\n", "labels": {"reads": [{"table": "rare_earth_companies", "columns": ["investor", "museum_id"]}], "writes": [{"table": "daily_revenue", "columns": ["investor", "museum_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT quantitysold, saleamount FROM dws.dws_coupon_use_hourly LIMIT 249;\nINSERT INTO bioprocess.engineering_projects SELECT stationid, vesselname FROM container WHERE stationid > 261;\nSQL\n", "labels": {"reads": [{"table": "dws.dws_coupon_use_hourly", "columns": ["quantitysold", "saleamount"]}, {"table": "container", "columns": ["stationid", "vesselname"]}], "writes": [{"table": "bioprocess.engineering_projects", "columns": ["stationid", "vesselname"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT peakhourid, customer_email_address FROM features LIMIT 363;\nINSERT INTO workersalaries SELECT service_details, address_line_2 FROM editor WHERE service_details > 125;\nSQL\n", "labels": {"reads": [{"table": "features", "columns": ["peakhourid", "customer_email_address"]}, {"table": "editor", "columns": ["service_details", "address_line_2"]}], "writes": [{"table": "workersalaries", "columns": ["service_details", "address_line_2"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"meals\").toPandas()\ndf[[\"sessiondate\", \"causename\"]].to_sql(\"all_star\", engine, index=False)\n", "labels": {"reads": [{"table": "meals", "columns": null}], "writes": [{"table": "all_star", "columns": ["sessiondate", "causename"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vessel_performance SELECT branch_id, acc_bal FROM footwear WHERE branch_id > 472\"\n", "labels": {"reads": [{"table": "footwear", "columns": ["branch_id", "acc_bal"]}], "writes": [{"table": "vessel_performance", "columns": ["branch_id", "acc_bal"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"us_platforms\").toPandas()\ndf[[\"hourid\", \"catalog_id\"]].to_sql(\"stg.orders_daily\", engine, index=False)\n", "labels": {"reads": [{"table": "us_platforms", "columns": null}], "writes": [{"table": "stg.orders_daily", "columns": ["hourid", "catalog_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"al_jazeera_data\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"submarine_canyons\")\n", "labels": {"reads": [{"table": "al_jazeera_data", "columns": null}], "writes": [{"table": "submarine_canyons", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO excavations SELECT genre, good_or_bad_customer FROM locations_oceania WHERE genre > 201\"\n", "labels": {"reads": [{"table": "locations_oceania", "columns": ["genre", "good_or_bad_customer"]}], "writes": [{"table": "excavations", "columns": ["genre", "good_or_bad_customer"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.member_point SELECT objectnumber, register_year FROM imagery_archive WHERE objectnumber > 19\"\n", "labels": {"reads": [{"table": "imagery_archive", "columns": ["objectnumber", "register_year"]}], "writes": [{"table": "ads.member_point", "columns": ["objectnumber", "register_year"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_dataset(ctx, \"military_bases\")\ndump_to_sink(df, \"higher_ed.publications\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "military_bases", "columns": null}], "writes": [{"table": "higher_ed.publications", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO bi_campaigns_delta SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO energy_production (num_projects, shop_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "energy_production", "columns": ["num_projects", "shop_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM communities\"\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO support_programs SELECT opening_hours, coalquantity, category_id, provider FROM daily_oil_production WHERE opening_hours > 35\"\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": ["opening_hours", "coalquantity", "category_id", "provider"]}], "writes": [{"table": "support_programs", "columns": ["opening_hours", "coalquantity", "category_id", "provider"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_categories\").toPandas()\ndf[[\"nid\", \"dept_id\"]].to_sql(\"ads.member_point\", engine, index=False)\n", "labels": {"reads": [{"table": "product_categories", "columns": null}], "writes": [{"table": "ads.member_point", "columns": ["nid", "dept_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO gender SELECT birthdate, residence, goldquantity FROM classicgame WHERE birthdate > 478;\nEOF\n", "labels": {"reads": [{"table": "classicgame", "columns": ["birthdate", "residence", "goldquantity"]}], "writes": [{"table": "gender", "columns": ["birthdate", "residence", "goldquantity"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO menu_engineering SELECT a.calendar, b.election_cycle FROM atlantic_plate a JOIN topublictransportation b ON a.attendance = b.attendance\"\n", "labels": {"reads": [{"table": "atlantic_plate", "columns": null}, {"table": "topublictransportation", "columns": null}], "writes": [{"table": "menu_engineering", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ironquantity, measurement_date FROM stg.stg_exposure_di LIMIT 329\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "stg.stg_exposure_di", "columns": ["ironquantity", "measurement_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"asset_parts\")\nsrc.write.insertInto(\"overwatch_scores\", overwrite=True)\n", "labels": {"reads": [{"table": "asset_parts", "columns": null}], "writes": [{"table": "overwatch_scores", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO transportation (trench_id, kills) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "transportation", "columns": ["trench_id", "kills"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO sustainability SELECT * FROM legacy\ncur.execute(\"SELECT party_email, personnel FROM climate_adaptation_re LIMIT 296\")\n", "labels": {"reads": [{"table": "climate_adaptation_re", "columns": ["party_email", "personnel"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO temperature SELECT negotiation_date, startdate, savingsid, away_team_points FROM southeast_providers WHERE negotiation_date > 403\")\n", "labels": {"reads": [{"table": "southeast_providers", "columns": ["negotiation_date", "startdate", "savingsid", "away_team_points"]}], "writes": [{"table": "temperature", "columns": ["negotiation_date", "startdate", "savingsid", "away_team_points"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_areas\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rural_areas", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sports SELECT participatedinesports, vessel_id FROM hotel_chains WHERE participatedinesports > 452;\nEOF\n", "labels": {"reads": [{"table": "hotel_chains", "columns": ["participatedinesports", "vessel_id"]}], "writes": [{"table": "sports", "columns": ["participatedinesports", "vessel_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO matches SELECT order_item_id, active_to_date, detention_type_description FROM dwd.dwd_orders_daily WHERE order_item_id > 197;\nEOF\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": ["order_item_id", "active_to_date", "detention_type_description"]}], "writes": [{"table": "matches", "columns": ["order_item_id", "active_to_date", "detention_type_description"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO multimodalhubs SELECT attorney, donor_name FROM art_exhibit_attendance WHERE attorney > 229\")\n", "labels": {"reads": [{"table": "art_exhibit_attendance", "columns": ["attorney", "donor_name"]}], "writes": [{"table": "multimodalhubs", "columns": ["attorney", "donor_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO bi.coupon_use SELECT period, volunteer_name, hours_developed FROM dwd.dwd_products WHERE period > 176\"\n", "labels": {"reads": [{"table": "dwd.dwd_products", "columns": ["period", "volunteer_name", "hours_developed"]}], "writes": [{"table": "bi.coupon_use", "columns": ["period", "volunteer_name", "hours_developed"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.exposure_hourly\").toPandas()\ndf[[\"word_count\", \"genderid\"]].to_sql(\"wearable_metrics\", engine, index=False)\n", "labels": {"reads": [{"table": "dwd.exposure_hourly", "columns": null}], "writes": [{"table": "wearable_metrics", "columns": ["word_count", "genderid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table australian_states --columns health_equity_metric_3,matchid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "australian_states", "columns": ["health_equity_metric_3", "matchid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"employees\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"show\")\n", "labels": {"reads": [{"table": "employees", "columns": null}], "writes": [{"table": "show", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO eco_hotels SELECT year_deforested, premise_id, meal_name FROM party WHERE year_deforested > 65\"\n", "labels": {"reads": [{"table": "party", "columns": ["year_deforested", "premise_id", "meal_name"]}], "writes": [{"table": "eco_hotels", "columns": ["year_deforested", "premise_id", "meal_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT technology, retailer_id FROM hair_care_sales LIMIT 369\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "hair_care_sales", "columns": ["technology", "retailer_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO healthcareaccess SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT song_year, amount_waste FROM conditions\", engine)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"basketball_teams\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "conditions", "columns": ["song_year", "amount_waste"]}], "writes": [{"table": "basketball_teams", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO vessel_safety SELECT depth, customer_id, createdate FROM mart.mart_coupon_use_delta WHERE depth > 497\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_delta", "columns": ["depth", "customer_id", "createdate"]}], "writes": [{"table": "vessel_safety", "columns": ["depth", "customer_id", "createdate"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO safetyincidents SELECT market_value, customer_id FROM traffic_citations WHERE market_value > 323\"\n", "labels": {"reads": [{"table": "traffic_citations", "columns": ["market_value", "customer_id"]}], "writes": [{"table": "safetyincidents", "columns": ["market_value", "customer_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT store_email_address, market_rate FROM brands\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"spaceexploration\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "brands", "columns": ["store_email_address", "market_rate"]}], "writes": [{"table": "spaceexploration", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO animal_budget SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO tourism SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"trainmaintenance\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_cart_item_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.dwd_cart_item_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_users_di SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT date_assigned_from, green_building_certified FROM mart.risk_score_df LIMIT 161;\nINSERT INTO playergamehistory SELECT date_of_latest_logon, accreditation_type, attendees FROM maintenance_requests WHERE date_of_latest_logon > 208;\nSQL\n", "labels": {"reads": [{"table": "mart.risk_score_df", "columns": ["date_assigned_from", "green_building_certified"]}, {"table": "maintenance_requests", "columns": ["date_of_latest_logon", "accreditation_type", "attendees"]}], "writes": [{"table": "playergamehistory", "columns": ["date_of_latest_logon", "accreditation_type", "attendees"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nset -euo pipefail\nhive -e \"INSERT INTO factories_africa SELECT prod_date, policyholder_id, max_speed FROM emerging_markets.digital_assets WHERE prod_date > 161\"\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": ["prod_date", "policyholder_id", "max_speed"]}], "writes": [{"table": "factories_africa", "columns": ["prod_date", "policyholder_id", "max_speed"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mediterranean_salinity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dws.cart_item_full\")\n", "labels": {"reads": [{"table": "mediterranean_salinity", "columns": null}], "writes": [{"table": "dws.cart_item_full", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sitem\").toPandas()\ndf[[\"product_subcategory\", \"purchasedate\"]].to_sql(\"victims\", engine, index=False)\n", "labels": {"reads": [{"table": "sitem", "columns": null}], "writes": [{"table": "victims", "columns": ["product_subcategory", "purchasedate"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO programs SELECT incident_type_description, wage FROM ai_ethics WHERE incident_type_description > 284;\nEOF\n", "labels": {"reads": [{"table": "ai_ethics", "columns": ["incident_type_description", "wage"]}], "writes": [{"table": "programs", "columns": ["incident_type_description", "wage"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT famous_title, missing_data FROM manufacturer LIMIT 121\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "manufacturer", "columns": ["famous_title", "missing_data"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO infrastructure_projects SELECT area_type, theftdate, card_type_code FROM staff WHERE area_type > 6;\nEOF\n", "labels": {"reads": [{"table": "staff", "columns": ["area_type", "theftdate", "card_type_code"]}], "writes": [{"table": "infrastructure_projects", "columns": ["area_type", "theftdate", "card_type_code"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO shariah_financing SELECT type, sustainability_id, supplier_country FROM public.police_calls WHERE type > 371\"\n", "labels": {"reads": [{"table": "public.police_calls", "columns": ["type", "sustainability_id", "supplier_country"]}], "writes": [{"table": "shariah_financing", "columns": ["type", "sustainability_id", "supplier_country"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ods.ods_campaigns_hourly (decision, claim_type) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_campaigns_hourly", "columns": ["decision", "claim_type"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO emergency_responses (stu_phone, permit_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "emergency_responses", "columns": ["stu_phone", "permit_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT size, sodium FROM accommodations LIMIT 92\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "accommodations", "columns": ["size", "sodium"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO sustainableproduction SELECT a.doctorsper1000, b.party FROM recycling_centers a JOIN uel_top10 b ON a.wage_increase = b.wage_increase\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recycling_centers", "columns": null}, {"table": "uel_top10", "columns": null}], "writes": [{"table": "sustainableproduction", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO dw.dw_inventory_df SELECT totaldonation, state_province FROM financial_capability_id WHERE totaldonation > 168\"\n", "labels": {"reads": [{"table": "financial_capability_id", "columns": ["totaldonation", "state_province"]}], "writes": [{"table": "dw.dw_inventory_df", "columns": ["totaldonation", "state_province"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO public.collected_fare SELECT sector, people_id, group_name, container_count FROM researchgrants WHERE sector > 5\"], check=True)\n", "labels": {"reads": [{"table": "researchgrants", "columns": ["sector", "people_id", "group_name", "container_count"]}], "writes": [{"table": "public.collected_fare", "columns": ["sector", "people_id", "group_name", "container_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO pilot SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT zip_code, therapy_session FROM street_markets LIMIT 406\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "street_markets", "columns": ["zip_code", "therapy_session"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO courts SELECT a.individual_last_name, b.next_maintenance FROM infrastructureprojects a JOIN accommodations b ON a.cvid = b.cvid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": null}, {"table": "accommodations", "columns": null}], "writes": [{"table": "courts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model galleries depends on user_workouts_march\ndbt run --select galleries --vars '{\"src\":\"user_workouts_march\"}'\n", "labels": {"reads": [{"table": "user_workouts_march", "columns": null}], "writes": [{"table": "galleries", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO materials SELECT a.pid, b.male_id FROM conservation_initiatives a JOIN initiatives b ON a.plan_type = b.plan_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "conservation_initiatives", "columns": null}, {"table": "initiatives", "columns": null}], "writes": [{"table": "materials", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO block SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"studies\")\ndump_to_store(df, \"dws.risk_score_daily\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "studies", "columns": null}], "writes": [{"table": "dws.risk_score_daily", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hours_served > 473).all()\n# src table: energy_consumption\nengine.execute(\"INSERT INTO org_volunteer SELECT * FROM energy_consumption\")\n", "labels": {"reads": [{"table": "energy_consumption", "columns": null}], "writes": [{"table": "org_volunteer", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO mart.mart_coupon_use_df SELECT * FROM legacy\ncur.execute(\"SELECT is_autonomous, city_area FROM ucl_top10 LIMIT 104\")\n", "labels": {"reads": [{"table": "ucl_top10", "columns": ["is_autonomous", "city_area"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO authors SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT founder, event_type FROM fruitimport LIMIT 64\")\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO cases SELECT vehicle, length, quantitysold, passengers FROM labour_productivity WHERE vehicle > 137\")\n", "labels": {"reads": [{"table": "fruitimport", "columns": ["founder", "event_type"]}, {"table": "labour_productivity", "columns": ["vehicle", "length", "quantitysold", "passengers"]}], "writes": [{"table": "cases", "columns": ["vehicle", "length", "quantitysold", "passengers"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO wholesale_orders SELECT stock, equipment_type FROM people WHERE stock > 101;\nEOF\n", "labels": {"reads": [{"table": "people", "columns": ["stock", "equipment_type"]}], "writes": [{"table": "wholesale_orders", "columns": ["stock", "equipment_type"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"route\")\nsrc.write.insertInto(\"product_characteristics\", overwrite=True)\n", "labels": {"reads": [{"table": "route", "columns": null}], "writes": [{"table": "product_characteristics", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table race_ethnicity --columns away_team_three_point,show_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "race_ethnicity", "columns": ["away_team_three_point", "show_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT country_name, transaction_value FROM militaryoperations LIMIT 20;\nINSERT INTO animal_populations SELECT manufacturername, roomtype, workoutdate, trench_id FROM culturalcompetency WHERE manufacturername > 242;\nSQL\n", "labels": {"reads": [{"table": "militaryoperations", "columns": ["country_name", "transaction_value"]}, {"table": "culturalcompetency", "columns": ["manufacturername", "roomtype", "workoutdate", "trench_id"]}], "writes": [{"table": "animal_populations", "columns": ["manufacturername", "roomtype", "workoutdate", "trench_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"food_safety_inspections\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"bi.clicks_df\")\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": null}], "writes": [{"table": "bi.clicks_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT kids, editor_id FROM city LIMIT 28\")\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO exhibition SELECT lawyer_name, customername FROM stg.stg_users_di WHERE lawyer_name > 228\")\n", "labels": {"reads": [{"table": "city", "columns": ["kids", "editor_id"]}, {"table": "stg.stg_users_di", "columns": ["lawyer_name", "customername"]}], "writes": [{"table": "exhibition", "columns": ["lawyer_name", "customername"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO royal_family (working_year_starts, decision) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "royal_family", "columns": ["working_year_starts", "decision"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model shipments_delta depends on league\ndbt run -s shipments_delta --vars 'source: league'\n", "labels": {"reads": [{"table": "league", "columns": null}], "writes": [{"table": "shipments_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.iata > 383).all()\n# src table: dwd.dwd_exposure_full\nengine.execute(\"INSERT INTO mart_vendors_full SELECT * FROM dwd.dwd_exposure_full\")\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": null}], "writes": [{"table": "mart_vendors_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT song_id, festival_name FROM dwd.dwd_orders_di\", engine)\nimport logging\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ndf.to_sql(\"recycling_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.dwd_orders_di", "columns": ["song_id", "festival_name"]}], "writes": [{"table": "recycling_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO crime_reports (restock_date, tripid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "crime_reports", "columns": ["restock_date", "tripid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO food_justice_contributors (sport, menu_category) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "food_justice_contributors", "columns": ["sport", "menu_category"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table dwd.exposure_hourly --columns number_thousands,borough --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "dwd.exposure_hourly", "columns": ["number_thousands", "borough"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.emp_dob > 119).all()\n# src table: battery_projects\nengine.execute(\"INSERT INTO climate_finance_organizations SELECT * FROM battery_projects\")\n", "labels": {"reads": [{"table": "battery_projects", "columns": null}], "writes": [{"table": "climate_finance_organizations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table medical_professionals --target-dir /tmp/land\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO healthcareaccess SELECT claim_amount, q1_2022_views FROM criminal_justice_reform_initiatives WHERE claim_amount > 3\"\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["claim_amount", "q1_2022_views"]}], "writes": [{"table": "healthcareaccess", "columns": ["claim_amount", "q1_2022_views"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO aus_wellbeing SELECT follows_ethical_practices, hours_served, highscore, sustainability_initiative_id FROM communitypolicing WHERE follows_ethical_practices > 202\"\n", "labels": {"reads": [{"table": "communitypolicing", "columns": ["follows_ethical_practices", "hours_served", "highscore", "sustainability_initiative_id"]}], "writes": [{"table": "aus_wellbeing", "columns": ["follows_ethical_practices", "hours_served", "highscore", "sustainability_initiative_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ads --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ads", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM art_pieces\", conn)\ndf.to_sql(\"employeepromotions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": [{"table": "employeepromotions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table customerorders --columns total,assistingnurse --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "customerorders", "columns": ["total", "assistingnurse"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"middle_east_military_spending\").toPandas()\ndf[[\"longitude\", \"feedid\"]].to_sql(\"space_missions\", engine, index=False)\n", "labels": {"reads": [{"table": "middle_east_military_spending", "columns": null}], "writes": [{"table": "space_missions", "columns": ["longitude", "feedid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT productid, ihsaa_football_class FROM people_addresses LIMIT 251;\nINSERT INTO supplier_addresses SELECT artifact_weight, department_id FROM dws.dws_coupon_use_full WHERE artifact_weight > 484;\nSQL\n", "labels": {"reads": [{"table": "people_addresses", "columns": ["productid", "ihsaa_football_class"]}, {"table": "dws.dws_coupon_use_full", "columns": ["artifact_weight", "department_id"]}], "writes": [{"table": "supplier_addresses", "columns": ["artifact_weight", "department_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO initiative_types SELECT 1\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.alert_id > 126).all()\n# src table: classrooms\nengine.execute(\"INSERT INTO imagery_archive SELECT * FROM classrooms\")\n", "labels": {"reads": [{"table": "classrooms", "columns": null}], "writes": [{"table": "imagery_archive", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"chemicals_annual\")\nsrc.write.insertInto(\"mental_health_parity\", overwrite=True)\n", "labels": {"reads": [{"table": "chemicals_annual", "columns": null}], "writes": [{"table": "mental_health_parity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table subjects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "subjects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO soccer_goals SELECT region_id, green_building_id FROM pollution_control_initiatives WHERE region_id > 128\"\n", "labels": {"reads": [{"table": "pollution_control_initiatives", "columns": ["region_id", "green_building_id"]}], "writes": [{"table": "soccer_goals", "columns": ["region_id", "green_building_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.orders_daily (license_number, factory_name) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.orders_daily", "columns": ["license_number", "factory_name"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO transport (official_name, time_day) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "transport", "columns": ["official_name", "time_day"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spaceradar\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "spaceradar", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO co2emissions SELECT shipped_to, loan_amount, vendorname FROM financialwellbeing WHERE shipped_to > 494\"\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": ["shipped_to", "loan_amount", "vendorname"]}], "writes": [{"table": "co2emissions", "columns": ["shipped_to", "loan_amount", "vendorname"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT volunteer_year, race_ethnicity FROM safety_incidents\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"intelligencesatellites\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "safety_incidents", "columns": ["volunteer_year", "race_ethnicity"]}], "writes": [{"table": "intelligencesatellites", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO missions SELECT total, devices, claim_header_id, clubdesc FROM supportservices WHERE total > 344\"\n", "labels": {"reads": [{"table": "supportservices", "columns": ["total", "devices", "claim_header_id", "clubdesc"]}], "writes": [{"table": "missions", "columns": ["total", "devices", "claim_header_id", "clubdesc"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO immunizationrates SELECT * FROM legacy\ncur.execute(\"SELECT coalid, programtype FROM seafoodsouthafricakenya LIMIT 285\")\n", "labels": {"reads": [{"table": "seafoodsouthafricakenya", "columns": ["coalid", "programtype"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM draft_copies\", conn)\ndf.to_sql(\"aquaculture_farms\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "draft_copies", "columns": null}], "writes": [{"table": "aquaculture_farms", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO traffic SELECT a.date_of_enrolment, b.sentence_length FROM bi_products a JOIN vessel_incident_count b ON a.total_distance = b.total_distance\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi_products", "columns": null}, {"table": "vessel_incident_count", "columns": null}], "writes": [{"table": "traffic", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg.stg_users_daily --columns g_name,sustainabilityid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg.stg_users_daily", "columns": ["g_name", "sustainabilityid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"whale_sightings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"infantmortalitydata\")\n", "labels": {"reads": [{"table": "whale_sightings", "columns": null}], "writes": [{"table": "infantmortalitydata", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO agency_satellites SELECT governor, sculpture_name, products_this_year FROM incidents_by_month WHERE governor > 304;\nEOF\n", "labels": {"reads": [{"table": "incidents_by_month", "columns": ["governor", "sculpture_name", "products_this_year"]}], "writes": [{"table": "agency_satellites", "columns": ["governor", "sculpture_name", "products_this_year"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO decentralized_applications SELECT day_number, electoral_register_id, treatment_date, cargo FROM diversity_metrics WHERE day_number > 381;\nEOF\n", "labels": {"reads": [{"table": "diversity_metrics", "columns": ["day_number", "electoral_register_id", "treatment_date", "cargo"]}], "writes": [{"table": "decentralized_applications", "columns": ["day_number", "electoral_register_id", "treatment_date", "cargo"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table electric_vehicle_stats --target-dir /tmp/land\n", "labels": {"reads": [{"table": "electric_vehicle_stats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM paintings\"\n", "labels": {"reads": [{"table": "paintings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.member_point_full\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"artcontributors\")\n", "labels": {"reads": [{"table": "bi.member_point_full", "columns": null}], "writes": [{"table": "artcontributors", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"languagesatrisk\").toPandas()\ndf[[\"did\", \"project_date\"]].to_sql(\"multimodalhubs\", engine, index=False)\n", "labels": {"reads": [{"table": "languagesatrisk", "columns": null}], "writes": [{"table": "multimodalhubs", "columns": ["did", "project_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"students_enrollment\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"dishes\")\n", "labels": {"reads": [{"table": "students_enrollment", "columns": null}], "writes": [{"table": "dishes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO timber_production SELECT genderid, showid, shippingmethod, chip_model FROM therapy_sessions WHERE genderid > 58\"\n", "labels": {"reads": [{"table": "therapy_sessions", "columns": ["genderid", "showid", "shippingmethod", "chip_model"]}], "writes": [{"table": "timber_production", "columns": ["genderid", "showid", "shippingmethod", "chip_model"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO atlantic_ocean SELECT employeename, capacity_percentage FROM road_construction WHERE employeename > 129\"\n", "labels": {"reads": [{"table": "road_construction", "columns": ["employeename", "capacity_percentage"]}], "writes": [{"table": "atlantic_ocean", "columns": ["employeename", "capacity_percentage"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model num_employees depends on manufacturing_processes\ndbt build -s num_employees --vars '{\"source_table\":\"manufacturing_processes\"}'\n", "labels": {"reads": [{"table": "manufacturing_processes", "columns": null}], "writes": [{"table": "num_employees", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table factories --target-dir /tmp/land\n", "labels": {"reads": [{"table": "factories", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model cosmetics_sales depends on stg_payments_hourly\ndbt build -s cosmetics_sales --vars '{\"source_table\":\"stg_payments_hourly\"}'\n", "labels": {"reads": [{"table": "stg_payments_hourly", "columns": null}], "writes": [{"table": "cosmetics_sales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO donations SELECT a.end_date, b.facid FROM video_games a JOIN broadband_revenue b ON a.pilot_name = b.pilot_name\"\n", "labels": {"reads": [{"table": "video_games", "columns": null}, {"table": "broadband_revenue", "columns": null}], "writes": [{"table": "donations", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_reviews\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "product_reviews", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"satellite_deployment\")\nsrc.write.insertInto(\"mart_payments_df\", overwrite=True)\n", "labels": {"reads": [{"table": "satellite_deployment", "columns": null}], "writes": [{"table": "mart_payments_df", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO certifications SELECT start_time, vr_platform FROM irrigation_systems WHERE start_time > 228\"\n", "labels": {"reads": [{"table": "irrigation_systems", "columns": ["start_time", "vr_platform"]}], "writes": [{"table": "certifications", "columns": ["start_time", "vr_platform"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO episodes (garment, category_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "episodes", "columns": ["garment", "category_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO aquaculture_farms SELECT 1\"\nlogger.info(msg)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO farmer_details SELECT num_shariah_compliant_investments, assignment_date, framework_name, cuisine FROM game_scores WHERE num_shariah_compliant_investments > 30\"\n", "labels": {"reads": [{"table": "game_scores", "columns": ["num_shariah_compliant_investments", "assignment_date", "framework_name", "cuisine"]}], "writes": [{"table": "farmer_details", "columns": ["num_shariah_compliant_investments", "assignment_date", "framework_name", "cuisine"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT menu_item, production_rate FROM green_certification LIMIT 158\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO market_share SELECT program_expenses, athleteid, total, dock_status FROM musical WHERE program_expenses > 336\")\n", "labels": {"reads": [{"table": "green_certification", "columns": ["menu_item", "production_rate"]}, {"table": "musical", "columns": ["program_expenses", "athleteid", "total", "dock_status"]}], "writes": [{"table": "market_share", "columns": ["program_expenses", "athleteid", "total", "dock_status"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO mart.campaigns_full (premise_details, state_province_county) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "mart.campaigns_full", "columns": ["premise_details", "state_province_county"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT transaction_type_code, satelliteid FROM temperature_data LIMIT 1;\nINSERT INTO navalequipmentmaintenance SELECT carbon_offset_tons, materialid FROM stg.campaigns_df WHERE carbon_offset_tons > 157;\nSQL\n", "labels": {"reads": [{"table": "temperature_data", "columns": ["transaction_type_code", "satelliteid"]}, {"table": "stg.campaigns_df", "columns": ["carbon_offset_tons", "materialid"]}], "writes": [{"table": "navalequipmentmaintenance", "columns": ["carbon_offset_tons", "materialid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"waterconsumptionbyoperation\")\nsrc.write.insertInto(\"underwater_cables\", overwrite=True)\n", "labels": {"reads": [{"table": "waterconsumptionbyoperation", "columns": null}], "writes": [{"table": "underwater_cables", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO article_views SELECT fault_short_name, mineid, people_id FROM indian_ocean_fishingvessels WHERE fault_short_name > 406\")\n", "labels": {"reads": [{"table": "indian_ocean_fishingvessels", "columns": ["fault_short_name", "mineid", "people_id"]}], "writes": [{"table": "article_views", "columns": ["fault_short_name", "mineid", "people_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT party_theme, text_of_notes FROM galleryc\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"sustainability_initiatives\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "galleryc", "columns": ["party_theme", "text_of_notes"]}], "writes": [{"table": "sustainability_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_orders_daily --columns arrival_time,paperid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_orders_daily", "columns": ["arrival_time", "paperid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"game_sessions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"financialwellbeing\")\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": [{"table": "financialwellbeing", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"athlete_wellbeing\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "athlete_wellbeing", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT number_city_affected, crop FROM ngo_funding LIMIT 50\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ads_payments_di SELECT service, institution_name, request_id, episode_number FROM player_attributes WHERE service > 26\")\n", "labels": {"reads": [{"table": "ngo_funding", "columns": ["number_city_affected", "crop"]}, {"table": "player_attributes", "columns": ["service", "institution_name", "request_id", "episode_number"]}], "writes": [{"table": "ads_payments_di", "columns": ["service", "institution_name", "request_id", "episode_number"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bay_area_properties\", conn)\ndf.to_sql(\"ads.inventory_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bay_area_properties", "columns": null}], "writes": [{"table": "ads.inventory_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ticket_sales depends on zip_codes\ndbt run --select ticket_sales --vars 'source: zip_codes'\n", "labels": {"reads": [{"table": "zip_codes", "columns": null}], "writes": [{"table": "ticket_sales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table community_engagement --target-dir /tmp/land\n", "labels": {"reads": [{"table": "community_engagement", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT course_description, incident_type_code FROM ods.ods_campaigns_hourly LIMIT 352\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "ods.ods_campaigns_hourly", "columns": ["course_description", "incident_type_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO cybersecurity_incidents SELECT programoutcomeid, funding_received, festival_name, mappingname FROM sustainable_projects WHERE programoutcomeid > 384\"], check=True)\n", "labels": {"reads": [{"table": "sustainable_projects", "columns": ["programoutcomeid", "funding_received", "festival_name", "mappingname"]}], "writes": [{"table": "cybersecurity_incidents", "columns": ["programoutcomeid", "funding_received", "festival_name", "mappingname"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT asset_make, provider_id FROM vendorfabrics\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"defense_projects_sales\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "vendorfabrics", "columns": ["asset_make", "provider_id"]}], "writes": [{"table": "defense_projects_sales", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table defense_spending_3 --columns event_type,governor --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "defense_spending_3", "columns": ["event_type", "governor"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table coal_reserves --target-dir /tmp/land\n", "labels": {"reads": [{"table": "coal_reserves", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT total_amount_purchased, community FROM auto_show LIMIT 466\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "auto_show", "columns": ["total_amount_purchased", "community"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO digital_assets SELECT jul, attendance, maintenance_date, percentage_change FROM dw.member_point_daily WHERE jul > 316\"\n", "labels": {"reads": [{"table": "dw.member_point_daily", "columns": ["jul", "attendance", "maintenance_date", "percentage_change"]}], "writes": [{"table": "digital_assets", "columns": ["jul", "attendance", "maintenance_date", "percentage_change"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO disasters SELECT 1\"\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nset -euo pipefail\nhive -e \"INSERT INTO economic_diversification_efforts SELECT created_at, method_id FROM species_data WHERE created_at > 419\"\n", "labels": {"reads": [{"table": "species_data", "columns": ["created_at", "method_id"]}], "writes": [{"table": "economic_diversification_efforts", "columns": ["created_at", "method_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.problem_id > 297).all()\n# src table: regional_railways\nengine.execute(\"INSERT INTO diversification_projects SELECT * FROM regional_railways\")\n", "labels": {"reads": [{"table": "regional_railways", "columns": null}], "writes": [{"table": "diversification_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO stg_users_daily SELECT ship_name, union_member, length_meters FROM hydro_plants WHERE ship_name > 220\"\n", "labels": {"reads": [{"table": "hydro_plants", "columns": ["ship_name", "union_member", "length_meters"]}], "writes": [{"table": "stg_users_daily", "columns": ["ship_name", "union_member", "length_meters"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO daily_industrial_water_usage SELECT item_id, watch_time, patientid, scientist FROM artist_data WHERE item_id > 285\"\n", "labels": {"reads": [{"table": "artist_data", "columns": ["item_id", "watch_time", "patientid", "scientist"]}], "writes": [{"table": "daily_industrial_water_usage", "columns": ["item_id", "watch_time", "patientid", "scientist"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO bi.bi_payments_delta SELECT artist_name, district_id, health_equity_metric_2 FROM astronaut_medical_3 WHERE artist_name > 453\"\n", "labels": {"reads": [{"table": "astronaut_medical_3", "columns": ["artist_name", "district_id", "health_equity_metric_2"]}], "writes": [{"table": "bi.bi_payments_delta", "columns": ["artist_name", "district_id", "health_equity_metric_2"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT attendanceid, supportrepid FROM ads_payments_daily LIMIT 472\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ads_payments_daily", "columns": ["attendanceid", "supportrepid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO veteran_occupations SELECT outcome_code, severity, aid_name, spacecraft_model FROM vehicle_safety_testing WHERE outcome_code > 132\"\n", "labels": {"reads": [{"table": "vehicle_safety_testing", "columns": ["outcome_code", "severity", "aid_name", "spacecraft_model"]}], "writes": [{"table": "veteran_occupations", "columns": ["outcome_code", "severity", "aid_name", "spacecraft_model"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table heritage_sites --target-dir /tmp/land\n", "labels": {"reads": [{"table": "heritage_sites", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO road_construction SELECT mgr_start_date, vegan, year, used_kb FROM animal_population_status WHERE mgr_start_date > 331\"\n", "labels": {"reads": [{"table": "animal_population_status", "columns": ["mgr_start_date", "vegan", "year", "used_kb"]}], "writes": [{"table": "road_construction", "columns": ["mgr_start_date", "vegan", "year", "used_kb"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT plan_id, attorney FROM donations_insert_2 LIMIT 134;\nINSERT INTO cities SELECT theatrename, assessment_score FROM creativeais WHERE theatrename > 92;\nSQL\n", "labels": {"reads": [{"table": "donations_insert_2", "columns": ["plan_id", "attorney"]}, {"table": "creativeais", "columns": ["theatrename", "assessment_score"]}], "writes": [{"table": "cities", "columns": ["theatrename", "assessment_score"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO restaurant_type SELECT spectators, donation_date, gradepoint FROM creative_ai WHERE spectators > 475\"\n", "labels": {"reads": [{"table": "creative_ai", "columns": ["spectators", "donation_date", "gradepoint"]}], "writes": [{"table": "restaurant_type", "columns": ["spectators", "donation_date", "gradepoint"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO vessel_types (supplier, athlete_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "vessel_types", "columns": ["supplier", "athlete_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT vaccine_name, artifact_name FROM space_missions_2 LIMIT 202\")\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO chemical_composition SELECT performance_id, mappinglength, status_code FROM dws.inventory_daily WHERE performance_id > 321\")\n", "labels": {"reads": [{"table": "space_missions_2", "columns": ["vaccine_name", "artifact_name"]}, {"table": "dws.inventory_daily", "columns": ["performance_id", "mappinglength", "status_code"]}], "writes": [{"table": "chemical_composition", "columns": ["performance_id", "mappinglength", "status_code"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model co2_emissions depends on virtual_visitors\ndbt build --select co2_emissions --vars 'source: virtual_visitors'\n", "labels": {"reads": [{"table": "virtual_visitors", "columns": null}], "writes": [{"table": "co2_emissions", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM transportation_fleet\", conn)\ndf.to_sql(\"state_contracts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "transportation_fleet", "columns": null}], "writes": [{"table": "state_contracts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO mars_spacecraft SELECT a.inclusive_housing_policy, b.medical_condition FROM dwd.dwd_risk_score_delta a JOIN ytterbium_supply b ON a.theftdate = b.theftdate\"\n", "labels": {"reads": [{"table": "dwd.dwd_risk_score_delta", "columns": null}, {"table": "ytterbium_supply", "columns": null}], "writes": [{"table": "mars_spacecraft", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_vendors_di\").toPandas()\ndf[[\"bioreactor_id\", \"operationname\"]].to_sql(\"biosensor.patents\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_vendors_di", "columns": null}], "writes": [{"table": "biosensor.patents", "columns": ["bioreactor_id", "operationname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM food_safety_inspections\", conn)\ndf.to_sql(\"socialimpactinvestments\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "food_safety_inspections", "columns": null}], "writes": [{"table": "socialimpactinvestments", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT population, staff_gender FROM workout LIMIT 348\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "workout", "columns": ["population", "staff_gender"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO students_lifelong_learning SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT materialname, cancel_date FROM check_ins LIMIT 405\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "check_ins", "columns": ["materialname", "cancel_date"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT attribute_id, receipt_date FROM head LIMIT 461;\nINSERT INTO roles SELECT research_name, num_virtual_tours, hardware_model_name FROM language WHERE research_name > 330;\nSQL\n", "labels": {"reads": [{"table": "head", "columns": ["attribute_id", "receipt_date"]}, {"table": "language", "columns": ["research_name", "num_virtual_tours", "hardware_model_name"]}], "writes": [{"table": "roles", "columns": ["research_name", "num_virtual_tours", "hardware_model_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table market_trends --target-dir /tmp/land\n", "labels": {"reads": [{"table": "market_trends", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO nutritionfacts (policy_holder_id, inclusive_housing_policy) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "nutritionfacts", "columns": ["policy_holder_id", "inclusive_housing_policy"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO continents SELECT totaldonation, art, elevation, dlocation FROM wrestler WHERE totaldonation > 366\")\n", "labels": {"reads": [{"table": "wrestler", "columns": ["totaldonation", "art", "elevation", "dlocation"]}], "writes": [{"table": "continents", "columns": ["totaldonation", "art", "elevation", "dlocation"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ods_sessions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ods_sessions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO food_safety_inspections SELECT fleet_id, jobcategory, sector FROM cosmetics WHERE fleet_id > 364\"\n", "labels": {"reads": [{"table": "cosmetics", "columns": ["fleet_id", "jobcategory", "sector"]}], "writes": [{"table": "food_safety_inspections", "columns": ["fleet_id", "jobcategory", "sector"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO threats SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO thefts SELECT sensor_type, date_stored, blockfloor FROM satellite_missions_large WHERE sensor_type > 124\"\n", "labels": {"reads": [{"table": "satellite_missions_large", "columns": ["sensor_type", "date_stored", "blockfloor"]}], "writes": [{"table": "thefts", "columns": ["sensor_type", "date_stored", "blockfloor"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO exhibitionattendance SELECT exit_type, detected_at FROM public_participation WHERE exit_type > 487\")\n", "labels": {"reads": [{"table": "public_participation", "columns": ["exit_type", "detected_at"]}], "writes": [{"table": "exhibitionattendance", "columns": ["exit_type", "detected_at"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO electric_buses SELECT middle_name, date_of_completion, unit_name FROM intelligencesatellites WHERE middle_name > 64\"\n", "labels": {"reads": [{"table": "intelligencesatellites", "columns": ["middle_name", "date_of_completion", "unit_name"]}], "writes": [{"table": "electric_buses", "columns": ["middle_name", "date_of_completion", "unit_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT professional_development, outcome_id FROM league LIMIT 53\")\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO restorative_justice_center SELECT priceid, team_id, monthly_rental FROM faculty WHERE priceid > 401\")\n", "labels": {"reads": [{"table": "league", "columns": ["professional_development", "outcome_id"]}, {"table": "faculty", "columns": ["priceid", "team_id", "monthly_rental"]}], "writes": [{"table": "restorative_justice_center", "columns": ["priceid", "team_id", "monthly_rental"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model editor depends on public.developers\ndbt build -s editor --vars '{\"src\":\"public.developers\"}'\n", "labels": {"reads": [{"table": "public.developers", "columns": null}], "writes": [{"table": "editor", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT researcher, half FROM ads.member_point\", engine)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"races\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.member_point", "columns": ["researcher", "half"]}], "writes": [{"table": "races", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"healthcare_facilities\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"climate_finance_asia\")\n", "labels": {"reads": [{"table": "healthcare_facilities", "columns": null}], "writes": [{"table": "climate_finance_asia", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.market_share > 415).all()\n# src table: article_views\nengine.execute(\"INSERT INTO astronauts SELECT * FROM article_views\")\n", "labels": {"reads": [{"table": "article_views", "columns": null}], "writes": [{"table": "astronauts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"surveylocations\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ancient_artifacts\")\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}], "writes": [{"table": "ancient_artifacts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO timbersales SELECT monthlyactiveusers, enroll_grade, support_id, mental_health_rating FROM busmaintenance WHERE monthlyactiveusers > 120;\nEOF\n", "labels": {"reads": [{"table": "busmaintenance", "columns": ["monthlyactiveusers", "enroll_grade", "support_id", "mental_health_rating"]}], "writes": [{"table": "timbersales", "columns": ["monthlyactiveusers", "enroll_grade", "support_id", "mental_health_rating"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO product_review SELECT * FROM legacy\ncur.execute(\"SELECT fundingid, budget_in_billions FROM disasters LIMIT 307\")\n", "labels": {"reads": [{"table": "disasters", "columns": ["fundingid", "budget_in_billions"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organic_farms\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"vessels_2\")\n", "labels": {"reads": [{"table": "organic_farms", "columns": null}], "writes": [{"table": "vessels_2", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table student_tests_taken --columns products_last_year,complaint_type_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "student_tests_taken", "columns": ["products_last_year", "complaint_type_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sanctuary, dish_type FROM marine_life_research LIMIT 335\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "marine_life_research", "columns": ["sanctuary", "dish_type"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO student_access SELECT a.mean_sea_level_pressure_inches, b.wellbeing_score FROM menu_categories a JOIN subjects b ON a.incidentid = b.incidentid\"\n", "labels": {"reads": [{"table": "menu_categories", "columns": null}, {"table": "subjects", "columns": null}], "writes": [{"table": "student_access", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table eia_schedule --target-dir /tmp/land\n", "labels": {"reads": [{"table": "eia_schedule", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.energy_generated > 9).all()\n# src table: ads.ads_payments_hourly\nengine.execute(\"INSERT INTO bi_orders_daily SELECT * FROM ads.ads_payments_hourly\")\n", "labels": {"reads": [{"table": "ads.ads_payments_hourly", "columns": null}], "writes": [{"table": "bi_orders_daily", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO zip_codes SELECT rest_id, stu_fname FROM fabricinventory WHERE rest_id > 428\")\n", "labels": {"reads": [{"table": "fabricinventory", "columns": ["rest_id", "stu_fname"]}], "writes": [{"table": "zip_codes", "columns": ["rest_id", "stu_fname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM militaryoperations\", conn)\ndf.to_sql(\"coral_reefs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "militaryoperations", "columns": null}], "writes": [{"table": "coral_reefs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO cosmetics SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"inventory\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "inventory", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM acidification_data\"\n", "labels": {"reads": [{"table": "acidification_data", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT crime_date, chair_name FROM wastewater_facilities\", engine)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"gardens\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "wastewater_facilities", "columns": ["crime_date", "chair_name"]}], "writes": [{"table": "gardens", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT budget_allocation, omim FROM bi.events_delta LIMIT 52\")\nrows = cur.fetchall()\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": ["budget_allocation", "omim"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT used_kb, date_in_locaton_to FROM ods_products_delta LIMIT 82\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ods_products_delta", "columns": ["used_kb", "date_in_locaton_to"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM military_aircraft_maintenance\", conn)\ndf.to_sql(\"zipcodes\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "military_aircraft_maintenance", "columns": null}], "writes": [{"table": "zipcodes", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ecohousing (warehouse_name, don_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ecohousing", "columns": ["warehouse_name", "don_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM artpieces\"\n", "labels": {"reads": [{"table": "artpieces", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO concert_revenue SELECT trainingyear, ota_id FROM mining_operation WHERE trainingyear > 352;\nEOF\n", "labels": {"reads": [{"table": "mining_operation", "columns": ["trainingyear", "ota_id"]}], "writes": [{"table": "concert_revenue", "columns": ["trainingyear", "ota_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nspark.sql(\"INSERT INTO haircaresales SELECT num_employees, city_town FROM ais WHERE num_employees > 295\")\n", "labels": {"reads": [{"table": "ais", "columns": ["num_employees", "city_town"]}], "writes": [{"table": "haircaresales", "columns": ["num_employees", "city_town"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dw.dw_orders_hourly SELECT firstdonationdate, thefttypeid FROM criminal_justice_reform_initiatives WHERE firstdonationdate > 228\"], check=True)\n", "labels": {"reads": [{"table": "criminal_justice_reform_initiatives", "columns": ["firstdonationdate", "thefttypeid"]}], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["firstdonationdate", "thefttypeid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"farmer_details\")\nsrc.write.insertInto(\"eco_hotels\", overwrite=True)\n", "labels": {"reads": [{"table": "farmer_details", "columns": null}], "writes": [{"table": "eco_hotels", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"budget\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"labor_statistics\")\n", "labels": {"reads": [{"table": "budget", "columns": null}], "writes": [{"table": "labor_statistics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO oceania_countries SELECT a.carrier, b.beds FROM virtual_tour_revenue a JOIN companies_extended b ON a.routeid = b.routeid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "virtual_tour_revenue", "columns": null}, {"table": "companies_extended", "columns": null}], "writes": [{"table": "oceania_countries", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT schedule, streams FROM precision_farming_imagery LIMIT 480\")\nrows = cur.fetchall()\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "precision_farming_imagery", "columns": ["schedule", "streams"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_inventory_hourly\").toPandas()\ndf[[\"browser_id\", \"countryname\"]].to_sql(\"urban_initiatives\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_inventory_hourly", "columns": null}], "writes": [{"table": "urban_initiatives", "columns": ["browser_id", "countryname"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ethicalaibudget --columns volunteer_name,therapy_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ethicalaibudget", "columns": ["volunteer_name", "therapy_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO first_notification_of_loss SELECT donor_program, contractor_name, city_population, meeting_count FROM ads.refunds_delta WHERE donor_program > 393\"\n", "labels": {"reads": [{"table": "ads.refunds_delta", "columns": ["donor_program", "contractor_name", "city_population", "meeting_count"]}], "writes": [{"table": "first_notification_of_loss", "columns": ["donor_program", "contractor_name", "city_population", "meeting_count"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.dws_coupon_use_di SELECT * FROM legacy\ncur.execute(\"SELECT application, port FROM youth_fan_participation LIMIT 275\")\n", "labels": {"reads": [{"table": "youth_fan_participation", "columns": ["application", "port"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_valid_to, height_feet FROM performances\", engine)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"artists\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "performances", "columns": ["date_valid_to", "height_feet"]}], "writes": [{"table": "artists", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"government.region\").toPandas()\ndf[[\"route\", \"primaryaffiliation\"]].to_sql(\"exit_strategies\", engine, index=False)\n", "labels": {"reads": [{"table": "government.region", "columns": null}], "writes": [{"table": "exit_strategies", "columns": ["route", "primaryaffiliation"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO investments_esg SELECT subscriber_id, document_description, main_services, manager_id FROM visits WHERE subscriber_id > 392\"\n", "labels": {"reads": [{"table": "visits", "columns": ["subscriber_id", "document_description", "main_services", "manager_id"]}], "writes": [{"table": "investments_esg", "columns": ["subscriber_id", "document_description", "main_services", "manager_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"projectemployees\")\nsrc.write.insertInto(\"carbon_offset_south_america\", overwrite=True)\n", "labels": {"reads": [{"table": "projectemployees", "columns": null}], "writes": [{"table": "carbon_offset_south_america", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO stg.campaigns_df (formats, system_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "stg.campaigns_df", "columns": ["formats", "system_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_sales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "military_sales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO workplace_safety SELECT budget_million, points, tech_type, student_capacity FROM vehiclemodels WHERE budget_million > 332;\nEOF\n", "labels": {"reads": [{"table": "vehiclemodels", "columns": ["budget_million", "points", "tech_type", "student_capacity"]}], "writes": [{"table": "workplace_safety", "columns": ["budget_million", "points", "tech_type", "student_capacity"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO space_missions (arrival_time, invoice_number) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "space_missions", "columns": ["arrival_time", "invoice_number"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO ocean SELECT machine_id, gender_code, group_id FROM garmentproduction WHERE machine_id > 415\"\n", "labels": {"reads": [{"table": "garmentproduction", "columns": ["machine_id", "gender_code", "group_id"]}], "writes": [{"table": "ocean", "columns": ["machine_id", "gender_code", "group_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT average, assets_billion FROM drugs LIMIT 177;\nINSERT INTO sustainable_urban_properties_2 SELECT contract_start, attorneyid FROM dws.risk_score_daily WHERE contract_start > 367;\nSQL\n", "labels": {"reads": [{"table": "drugs", "columns": ["average", "assets_billion"]}, {"table": "dws.risk_score_daily", "columns": ["contract_start", "attorneyid"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["contract_start", "attorneyid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO support_groups SELECT cause, fiscal_year FROM mammals WHERE cause > 443\")\n", "labels": {"reads": [{"table": "mammals", "columns": ["cause", "fiscal_year"]}], "writes": [{"table": "support_groups", "columns": ["cause", "fiscal_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"government_transparency\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"malicious_activity\")\n", "labels": {"reads": [{"table": "government_transparency", "columns": null}], "writes": [{"table": "malicious_activity", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT shelter_name, trip_id FROM multimodalhubs LIMIT 333\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "multimodalhubs", "columns": ["shelter_name", "trip_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"functional_areas\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "functional_areas", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT first_donation_date, is_sustainable FROM incident\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"galleryc\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "incident", "columns": ["first_donation_date", "is_sustainable"]}], "writes": [{"table": "galleryc", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT label_id, building_manager FROM vessel_safety LIMIT 20;\nINSERT INTO products_booked SELECT energytype, cuisine_name, swimmer_id FROM mart.mart_member_point_df WHERE energytype > 262;\nSQL\n", "labels": {"reads": [{"table": "vessel_safety", "columns": ["label_id", "building_manager"]}, {"table": "mart.mart_member_point_df", "columns": ["energytype", "cuisine_name", "swimmer_id"]}], "writes": [{"table": "products_booked", "columns": ["energytype", "cuisine_name", "swimmer_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO expenses SELECT a.male_id, b.nurse FROM media_library a JOIN hr.employees b ON a.annual_interchanges = b.annual_interchanges\"\n", "labels": {"reads": [{"table": "media_library", "columns": null}, {"table": "hr.employees", "columns": null}], "writes": [{"table": "expenses", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM salinity_readings\", conn)\ndf.to_sql(\"danceevents\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "salinity_readings", "columns": null}], "writes": [{"table": "danceevents", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO bi.member_point_full SELECT inspectiondate, completed FROM yoga WHERE inspectiondate > 129;\nEOF\n", "labels": {"reads": [{"table": "yoga", "columns": ["inspectiondate", "completed"]}], "writes": [{"table": "bi.member_point_full", "columns": ["inspectiondate", "completed"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT article_id, departmentid FROM australian_states LIMIT 295;\nINSERT INTO dws.dws_inventory_hourly SELECT domestic_passengers, assigned_to_staff_id, screening, location_id FROM open_data_initiatives WHERE domestic_passengers > 404;\nSQL\n", "labels": {"reads": [{"table": "australian_states", "columns": ["article_id", "departmentid"]}, {"table": "open_data_initiatives", "columns": ["domestic_passengers", "assigned_to_staff_id", "screening", "location_id"]}], "writes": [{"table": "dws.dws_inventory_hourly", "columns": ["domestic_passengers", "assigned_to_staff_id", "screening", "location_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.payments_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"heritagesites\")\n", "labels": {"reads": [{"table": "bi.payments_daily", "columns": null}], "writes": [{"table": "heritagesites", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO transportation_fleet SELECT other_details, strain_name, cb_year, plan_id FROM news_report WHERE other_details > 34\"\n", "labels": {"reads": [{"table": "news_report", "columns": ["other_details", "strain_name", "cb_year", "plan_id"]}], "writes": [{"table": "transportation_fleet", "columns": ["other_details", "strain_name", "cb_year", "plan_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"prison\")\nsave_to_sink(df, \"browser\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "prison", "columns": null}], "writes": [{"table": "browser", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO rural_hospitals SELECT dphone, salary, truck_licence_number, minister FROM papers WHERE dphone > 73\"\n", "labels": {"reads": [{"table": "papers", "columns": ["dphone", "salary", "truck_licence_number", "minister"]}], "writes": [{"table": "rural_hospitals", "columns": ["dphone", "salary", "truck_licence_number", "minister"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO org_donation SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO stg.inventory_df SELECT a.analysis_date, b.galleryid FROM digital_trends a JOIN team_franchise b ON a.round_amount = b.round_amount\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}, {"table": "team_franchise", "columns": null}], "writes": [{"table": "stg.inventory_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tencel_sources SELECT claim_status_name, black, causename, number_thousands FROM city_tech WHERE claim_status_name > 291\"\n", "labels": {"reads": [{"table": "city_tech", "columns": ["claim_status_name", "black", "causename", "number_thousands"]}], "writes": [{"table": "tencel_sources", "columns": ["claim_status_name", "black", "causename", "number_thousands"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT outcome_code, station FROM founder LIMIT 387\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO artifact_analysis SELECT org, emergency_type FROM artcontributors WHERE org > 130\")\n", "labels": {"reads": [{"table": "founder", "columns": ["outcome_code", "station"]}, {"table": "artcontributors", "columns": ["org", "emergency_type"]}], "writes": [{"table": "artifact_analysis", "columns": ["org", "emergency_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"communities\")\nexport_to_target(df, \"smart_cities\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "communities", "columns": null}], "writes": [{"table": "smart_cities", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"galleryc\").toPandas()\ndf[[\"discount\", \"faculty_id\"]].to_sql(\"mart.device_log_hourly\", engine, index=False)\n", "labels": {"reads": [{"table": "galleryc", "columns": null}], "writes": [{"table": "mart.device_log_hourly", "columns": ["discount", "faculty_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model waste_generation depends on vr_adopters\ndbt build --models waste_generation --vars '{\"source_table\":\"vr_adopters\"}'\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": [{"table": "waste_generation", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.courtid > 36).all()\n# src table: districts_india\nengine.execute(\"INSERT INTO stores_2 SELECT * FROM districts_india\")\n", "labels": {"reads": [{"table": "districts_india", "columns": null}], "writes": [{"table": "stores_2", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_input(ctx, \"restorative_justice_sentences\")\npush_to_sink(df, \"stg.refunds\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "restorative_justice_sentences", "columns": null}], "writes": [{"table": "stg.refunds", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO community_centers SELECT union_member_id, building_address FROM dwd.dwd_exposure_full WHERE union_member_id > 436\"\n", "labels": {"reads": [{"table": "dwd.dwd_exposure_full", "columns": ["union_member_id", "building_address"]}], "writes": [{"table": "community_centers", "columns": ["union_member_id", "building_address"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"cosmetic_formula\")\nsrc.write.insertInto(\"bi.bi_vendors_di\", overwrite=True)\n", "labels": {"reads": [{"table": "cosmetic_formula", "columns": null}], "writes": [{"table": "bi.bi_vendors_di", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT production_mwh, star_rating_description FROM trips LIMIT 258\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nimport logging\n", "labels": {"reads": [{"table": "trips", "columns": ["production_mwh", "star_rating_description"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"student\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"professional_development\")\n", "labels": {"reads": [{"table": "student", "columns": null}], "writes": [{"table": "professional_development", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model flu_cases depends on bi.bi_member_point\ndbt run -s flu_cases --vars '{\"src\":\"bi.bi_member_point\"}'\n", "labels": {"reads": [{"table": "bi.bi_member_point", "columns": null}], "writes": [{"table": "flu_cases", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO criminal_cases SELECT * FROM legacy\ncur.execute(\"SELECT purchases, fundingamount FROM water_distribution LIMIT 148\")\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["purchases", "fundingamount"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.video_id > 73).all()\n# src table: travel_advisory\nengine.execute(\"INSERT INTO city_budgets SELECT * FROM travel_advisory\")\n", "labels": {"reads": [{"table": "travel_advisory", "columns": null}], "writes": [{"table": "city_budgets", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT feature_id, event_type_id FROM dw.users_hourly\", engine)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\ndf.to_sql(\"police_stations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dw.users_hourly", "columns": ["feature_id", "event_type_id"]}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"videos\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"displaced_people\")\n", "labels": {"reads": [{"table": "videos", "columns": null}], "writes": [{"table": "displaced_people", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO cargos SELECT ticket_price, discovered_date, denomination, profession_count FROM request WHERE ticket_price > 24\"\n", "labels": {"reads": [{"table": "request", "columns": ["ticket_price", "discovered_date", "denomination", "profession_count"]}], "writes": [{"table": "cargos", "columns": ["ticket_price", "discovered_date", "denomination", "profession_count"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT next_maintenance, grape FROM tb_cases LIMIT 263\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO field_production SELECT detention_type_description, operation_count FROM players WHERE detention_type_description > 399\")\n", "labels": {"reads": [{"table": "tb_cases", "columns": ["next_maintenance", "grape"]}, {"table": "players", "columns": ["detention_type_description", "operation_count"]}], "writes": [{"table": "field_production", "columns": ["detention_type_description", "operation_count"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO therapy SELECT * FROM legacy\ncur.execute(\"SELECT protein_name, production_cost FROM tourist_attractions LIMIT 253\")\n", "labels": {"reads": [{"table": "tourist_attractions", "columns": ["protein_name", "production_cost"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO patents SELECT metric_id, artist_gender, region_code FROM casesbyyear WHERE metric_id > 231\"], check=True)\n", "labels": {"reads": [{"table": "casesbyyear", "columns": ["metric_id", "artist_gender", "region_code"]}], "writes": [{"table": "patents", "columns": ["metric_id", "artist_gender", "region_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM ads.ads_shipments_delta\", conn)\ndf.to_sql(\"swimmer\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_shipments_delta", "columns": null}], "writes": [{"table": "swimmer", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO recreation_centers SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO eco_diversification_investment (socialimpactscore, lot_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "eco_diversification_investment", "columns": ["socialimpactscore", "lot_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO provinces (productiondate, address_details) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "provinces", "columns": ["productiondate", "address_details"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO prereq SELECT 1\"\nlogger.info(msg)\nimport logging\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO production_yearly SELECT a.annual_interchanges, b.rank_in_round FROM indie_artists a JOIN stg.orders_daily b ON a.musical_id = b.musical_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "indie_artists", "columns": null}, {"table": "stg.orders_daily", "columns": null}], "writes": [{"table": "production_yearly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dwd_payments_full depends on family_cases\ndbt build --select dwd_payments_full --vars '{\"source_table\":\"family_cases\"}'\n", "labels": {"reads": [{"table": "family_cases", "columns": null}], "writes": [{"table": "dwd_payments_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"artist_info\")\nsrc.write.insertInto(\"artsandcrafts\", overwrite=True)\n", "labels": {"reads": [{"table": "artist_info", "columns": null}], "writes": [{"table": "artsandcrafts", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO ai_safety SELECT class_section, acidity FROM fair_trade_brands WHERE class_section > 86\")\n", "labels": {"reads": [{"table": "fair_trade_brands", "columns": ["class_section", "acidity"]}], "writes": [{"table": "ai_safety", "columns": ["class_section", "acidity"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO malicious_activity SELECT a.grantid, b.date_of_notes FROM ods.vendors_di a JOIN program b ON a.clubname = b.clubname\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.vendors_di", "columns": null}, {"table": "program", "columns": null}], "writes": [{"table": "malicious_activity", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO cosmetics.lipstick_spf_data (vehicle_name, number_of_hosts) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "cosmetics.lipstick_spf_data", "columns": ["vehicle_name", "number_of_hosts"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"supportprograms\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "supportprograms", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table product_revenue --columns cultural_significance,trip_end_time --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "product_revenue", "columns": ["cultural_significance", "trip_end_time"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.payment_method > 72).all()\n# src table: spacecraftmanufacturing\nengine.execute(\"INSERT INTO ads.ads_vendors_hourly SELECT * FROM spacecraftmanufacturing\")\n", "labels": {"reads": [{"table": "spacecraftmanufacturing", "columns": null}], "writes": [{"table": "ads.ads_vendors_hourly", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"counties\")\npush_to_warehouse(df, \"green_buildings\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "counties", "columns": null}], "writes": [{"table": "green_buildings", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO average (project_date, physician) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "average", "columns": ["project_date", "physician"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO stg.campaigns_df SELECT a.policy_description, b.network FROM london.stations a JOIN fair_trade_brands b ON a.shipment_year = b.shipment_year\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "london.stations", "columns": null}, {"table": "fair_trade_brands", "columns": null}], "writes": [{"table": "stg.campaigns_df", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO canals SELECT unsure_rate, hotel_chain_name, order_item_status, volunteerid FROM cargo_handling WHERE unsure_rate > 3\"\n", "labels": {"reads": [{"table": "cargo_handling", "columns": ["unsure_rate", "hotel_chain_name", "order_item_status", "volunteerid"]}], "writes": [{"table": "canals", "columns": ["unsure_rate", "hotel_chain_name", "order_item_status", "volunteerid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO crime_stats SELECT tonnage, framework, orderdate, hotel_id FROM artist WHERE tonnage > 104\"], check=True)\n", "labels": {"reads": [{"table": "artist", "columns": ["tonnage", "framework", "orderdate", "hotel_id"]}], "writes": [{"table": "crime_stats", "columns": ["tonnage", "framework", "orderdate", "hotel_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"concert_events\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marinespeciesobservations\")\n", "labels": {"reads": [{"table": "concert_events", "columns": null}], "writes": [{"table": "marinespeciesobservations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO head SELECT q1_2022_views, gdp, policy_type_code FROM wearable_metrics WHERE q1_2022_views > 331\"\n", "labels": {"reads": [{"table": "wearable_metrics", "columns": ["q1_2022_views", "gdp", "policy_type_code"]}], "writes": [{"table": "head", "columns": ["q1_2022_views", "gdp", "policy_type_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ai_ethics\"\n", "labels": {"reads": [{"table": "ai_ethics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO rural_infrastructure SELECT * FROM legacy\ncur.execute(\"SELECT is_public, inclusive FROM infrastructure_projects LIMIT 227\")\n", "labels": {"reads": [{"table": "infrastructure_projects", "columns": ["is_public", "inclusive"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.well_type > 59).all()\n# src table: bi_device_log_daily\nengine.execute(\"INSERT INTO train_lines SELECT * FROM bi_device_log_daily\")\n", "labels": {"reads": [{"table": "bi_device_log_daily", "columns": null}], "writes": [{"table": "train_lines", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT org_id, sector FROM ancient_cultures LIMIT 227\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ancient_cultures", "columns": ["org_id", "sector"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nspark.sql(\"INSERT INTO election SELECT conferenceid, waste_generation, operationtype FROM dws.dws_campaigns_df WHERE conferenceid > 225\")\n", "labels": {"reads": [{"table": "dws.dws_campaigns_df", "columns": ["conferenceid", "waste_generation", "operationtype"]}], "writes": [{"table": "election", "columns": ["conferenceid", "waste_generation", "operationtype"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ads.ads_member_point_daily SELECT violationtype, culture, max_gust_speed_mph, causename FROM dws.dws_orders WHERE violationtype > 131\"\n", "labels": {"reads": [{"table": "dws.dws_orders", "columns": ["violationtype", "culture", "max_gust_speed_mph", "causename"]}], "writes": [{"table": "ads.ads_member_point_daily", "columns": ["violationtype", "culture", "max_gust_speed_mph", "causename"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM broadband_providers\", conn)\ndf.to_sql(\"game_results\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "broadband_providers", "columns": null}], "writes": [{"table": "game_results", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi.bi_inventory --columns drought_id,typeid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_inventory", "columns": ["drought_id", "typeid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"singer_in_concert\")\nsrc.write.insertInto(\"companies\", overwrite=True)\n", "labels": {"reads": [{"table": "singer_in_concert", "columns": null}], "writes": [{"table": "companies", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO prescribes SELECT a.opening_hours, b.signup_date FROM labor_cost a JOIN host b ON a.product_id = b.product_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "labor_cost", "columns": null}, {"table": "host", "columns": null}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO org_climate_finance SELECT color_code, store_phone, claimtype, other_hotel_details FROM provinces WHERE color_code > 245\"\n", "labels": {"reads": [{"table": "provinces", "columns": ["color_code", "store_phone", "claimtype", "other_hotel_details"]}], "writes": [{"table": "org_climate_finance", "columns": ["color_code", "store_phone", "claimtype", "other_hotel_details"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table rd_expenditure --target-dir /tmp/land\n", "labels": {"reads": [{"table": "rd_expenditure", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO train_lines SELECT * FROM legacy\ncur.execute(\"SELECT region, hotel_chain_name FROM prescribes LIMIT 316\")\n", "labels": {"reads": [{"table": "prescribes", "columns": ["region", "hotel_chain_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO satellitedata SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO revenue (operation_type, contributionid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "revenue", "columns": ["operation_type", "contributionid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"transportation_per_country\").toPandas()\ndf[[\"storename\", \"spacecraft_model\"]].to_sql(\"us_cities\", engine, index=False)\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": null}], "writes": [{"table": "us_cities", "columns": ["storename", "spacecraft_model"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO province.human_rights_data SELECT * FROM legacy\ncur.execute(\"SELECT extraction_amount, enr FROM animal_population_status LIMIT 66\")\n", "labels": {"reads": [{"table": "animal_population_status", "columns": ["extraction_amount", "enr"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT program_id, painting_name FROM player_f LIMIT 454\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO customer_policies SELECT person_name, site_id, low_temperature FROM grapes WHERE person_name > 120\")\n", "labels": {"reads": [{"table": "player_f", "columns": ["program_id", "painting_name"]}, {"table": "grapes", "columns": ["person_name", "site_id", "low_temperature"]}], "writes": [{"table": "customer_policies", "columns": ["person_name", "site_id", "low_temperature"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"contracts\").toPandas()\ndf[[\"entryid\", \"is_organic\"]].to_sql(\"customer\", engine, index=False)\n", "labels": {"reads": [{"table": "contracts", "columns": null}], "writes": [{"table": "customer", "columns": ["entryid", "is_organic"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO inclusion_efforts (trainingtype, building_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "inclusion_efforts", "columns": ["trainingtype", "building_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model train_station depends on vr_adopters\ndbt run --models train_station --vars 'source: vr_adopters'\n", "labels": {"reads": [{"table": "vr_adopters", "columns": null}], "writes": [{"table": "train_station", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO community_events SELECT provider_name, building_id, decision, shipment_id FROM workshop WHERE provider_name > 400\"], check=True)\n", "labels": {"reads": [{"table": "workshop", "columns": ["provider_name", "building_id", "decision", "shipment_id"]}], "writes": [{"table": "community_events", "columns": ["provider_name", "building_id", "decision", "shipment_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"policy_advocacy\").toPandas()\ndf[[\"time_id\", \"strat_id\"]].to_sql(\"daily_articles_by_category\", engine, index=False)\n", "labels": {"reads": [{"table": "policy_advocacy", "columns": null}], "writes": [{"table": "daily_articles_by_category", "columns": ["time_id", "strat_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM shariah_compliant_finance\", conn)\ndf.to_sql(\"innovation_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "shariah_compliant_finance", "columns": null}], "writes": [{"table": "innovation_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mineral_extraction --columns sport_id,tournament_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mineral_extraction", "columns": ["sport_id", "tournament_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO research_staff SELECT forest_type, apid, cases_handled, u_id FROM bike_share WHERE forest_type > 356\")\n", "labels": {"reads": [{"table": "bike_share", "columns": ["forest_type", "apid", "cases_handled", "u_id"]}], "writes": [{"table": "research_staff", "columns": ["forest_type", "apid", "cases_handled", "u_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT factory_id, genre FROM europium_exports LIMIT 102\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "europium_exports", "columns": ["factory_id", "genre"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table chemical_production_5 --target-dir /tmp/land\n", "labels": {"reads": [{"table": "chemical_production_5", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO fossil_fuel_vehicles_japan (seal_species, health_equity_metric_2) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "fossil_fuel_vehicles_japan", "columns": ["seal_species", "health_equity_metric_2"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"invoices\")\nsrc.write.insertInto(\"port\", overwrite=True)\n", "labels": {"reads": [{"table": "invoices", "columns": null}], "writes": [{"table": "port", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"province.human_rights_data\")\nsrc.write.insertInto(\"programs\", overwrite=True)\n", "labels": {"reads": [{"table": "province.human_rights_data", "columns": null}], "writes": [{"table": "programs", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tree_species SELECT spacecraft_id, property_id, drug_name FROM faculty_participates_in WHERE spacecraft_id > 400\"\n", "labels": {"reads": [{"table": "faculty_participates_in", "columns": ["spacecraft_id", "property_id", "drug_name"]}], "writes": [{"table": "tree_species", "columns": ["spacecraft_id", "property_id", "drug_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO bus_routes SELECT a.asset_model, b.average_age FROM workforce a JOIN rent_arrears b ON a.supplierid = b.supplierid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "workforce", "columns": null}, {"table": "rent_arrears", "columns": null}], "writes": [{"table": "bus_routes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table levees --columns location_code,socially_responsible --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "levees", "columns": ["location_code", "socially_responsible"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_source(ctx, \"customer\")\npush_to_warehouse(df, \"dw_vendors_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "customer", "columns": null}], "writes": [{"table": "dw_vendors_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.advisoryid > 216).all()\n# src table: conservation_programs\nengine.execute(\"INSERT INTO carbon_offset_programs SELECT * FROM conservation_programs\")\n", "labels": {"reads": [{"table": "conservation_programs", "columns": null}], "writes": [{"table": "carbon_offset_programs", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO branch SELECT fouls, transactions, is_deforested, community_type FROM waterconservationbudget WHERE fouls > 224\"\n", "labels": {"reads": [{"table": "waterconservationbudget", "columns": ["fouls", "transactions", "is_deforested", "community_type"]}], "writes": [{"table": "branch", "columns": ["fouls", "transactions", "is_deforested", "community_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"products\").toPandas()\ndf[[\"rank_in_round\", \"farm_id\"]].to_sql(\"fields\", engine, index=False)\n", "labels": {"reads": [{"table": "products", "columns": null}], "writes": [{"table": "fields", "columns": ["rank_in_round", "farm_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"waterconservationinitiatives\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "waterconservationinitiatives", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO socially_responsible_loans SELECT porphyria, party FROM volunteer_registration WHERE porphyria > 350\"\n", "labels": {"reads": [{"table": "volunteer_registration", "columns": ["porphyria", "party"]}], "writes": [{"table": "socially_responsible_loans", "columns": ["porphyria", "party"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO incarcerated SELECT pressure, labor_id FROM volunteer_signups WHERE pressure > 200\"\n", "labels": {"reads": [{"table": "volunteer_signups", "columns": ["pressure", "labor_id"]}], "writes": [{"table": "incarcerated", "columns": ["pressure", "labor_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT partid, art_movement FROM co2price LIMIT 72\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "co2price", "columns": ["partid", "art_movement"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table safety_research --columns causeid,left_office --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "safety_research", "columns": ["causeid", "left_office"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO time_dim SELECT member, provider_id, recycling_rate, service_type_code FROM carbon_offset_projects WHERE member > 410\"\n", "labels": {"reads": [{"table": "carbon_offset_projects", "columns": ["member", "provider_id", "recycling_rate", "service_type_code"]}], "writes": [{"table": "time_dim", "columns": ["member", "provider_id", "recycling_rate", "service_type_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO rare_earth_companies SELECT task_details, request_date, transaction_type FROM stock_levels WHERE task_details > 485\"\n", "labels": {"reads": [{"table": "stock_levels", "columns": ["task_details", "request_date", "transaction_type"]}], "writes": [{"table": "rare_earth_companies", "columns": ["task_details", "request_date", "transaction_type"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO organic_products SELECT gender_group, donator_name FROM cargo_equipment WHERE gender_group > 254\"\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": ["gender_group", "donator_name"]}], "writes": [{"table": "organic_products", "columns": ["gender_group", "donator_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"patents\")\nsrc.write.insertInto(\"financial_capability\", overwrite=True)\n", "labels": {"reads": [{"table": "patents", "columns": null}], "writes": [{"table": "financial_capability", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO city_waste_generation SELECT contactid, donorname, count_date FROM reo_production WHERE contactid > 394\"\n", "labels": {"reads": [{"table": "reo_production", "columns": ["contactid", "donorname", "count_date"]}], "writes": [{"table": "city_waste_generation", "columns": ["contactid", "donorname", "count_date"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT partid, fate FROM category_revenue\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\ndf.to_sql(\"diversification_projects\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "category_revenue", "columns": ["partid", "fate"]}], "writes": [{"table": "diversification_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO programoutcomes SELECT stu_fname, dribbling FROM management WHERE stu_fname > 49\"\n", "labels": {"reads": [{"table": "management", "columns": ["stu_fname", "dribbling"]}], "writes": [{"table": "programoutcomes", "columns": ["stu_fname", "dribbling"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT head_id, awayteamid FROM sports LIMIT 118\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "sports", "columns": ["head_id", "awayteamid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO country_landfill_capacity SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.airport_id > 263).all()\n# src table: conservation_projects\nengine.execute(\"INSERT INTO tree_species SELECT * FROM conservation_projects\")\n", "labels": {"reads": [{"table": "conservation_projects", "columns": null}], "writes": [{"table": "tree_species", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO bi.bi_orders_delta SELECT * FROM legacy\ncur.execute(\"SELECT dob, fan_id FROM heritage_sites LIMIT 131\")\n", "labels": {"reads": [{"table": "heritage_sites", "columns": ["dob", "fan_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO biosensors.projects SELECT a.sustainability_id, b.dance_form FROM game_scores a JOIN technology_access b ON a.share_in_percent = b.share_in_percent\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "game_scores", "columns": null}, {"table": "technology_access", "columns": null}], "writes": [{"table": "biosensors.projects", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ods_member_point_full depends on trip\ndbt run -s ods_member_point_full --vars '{\"source_table\":\"trip\"}'\n", "labels": {"reads": [{"table": "trip", "columns": null}], "writes": [{"table": "ods_member_point_full", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table seafoodsouthafricakenya --columns routename,meal_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "seafoodsouthafricakenya", "columns": ["routename", "meal_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO military_expenditure SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO tracklists SELECT testtypeid, facilityid, clubid, time_month FROM stats WHERE testtypeid > 57\"\n", "labels": {"reads": [{"table": "stats", "columns": ["testtypeid", "facilityid", "clubid", "time_month"]}], "writes": [{"table": "tracklists", "columns": ["testtypeid", "facilityid", "clubid", "time_month"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO exhibition_visitors SELECT 1\"\nset -euo pipefail\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM water_usage\", conn)\ndf.to_sql(\"provinces\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "water_usage", "columns": null}], "writes": [{"table": "provinces", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wastegeneration\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dwd_risk_score_hourly\")\n", "labels": {"reads": [{"table": "wastegeneration", "columns": null}], "writes": [{"table": "dwd_risk_score_hourly", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO match_result SELECT runtime, max_salary FROM climate_finance_asia WHERE runtime > 75;\nEOF\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": ["runtime", "max_salary"]}], "writes": [{"table": "match_result", "columns": ["runtime", "max_salary"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"english_premier_league\")\nwrite_to_warehouse(df, \"region_stats\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "english_premier_league", "columns": null}], "writes": [{"table": "region_stats", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO transportation_union SELECT * FROM legacy\ncur.execute(\"SELECT start_therapy, clean_jerk FROM district_schools LIMIT 139\")\n", "labels": {"reads": [{"table": "district_schools", "columns": ["start_therapy", "clean_jerk"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO postseason (no_of_customers, height) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "postseason", "columns": ["no_of_customers", "height"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM militarydrones\"\n", "labels": {"reads": [{"table": "militarydrones", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT wrestler_id, claimdate FROM ports LIMIT 28\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ports", "columns": ["wrestler_id", "claimdate"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.dw_sessions_full\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dw.dw_sessions_full", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.outcome_code > 298).all()\n# src table: mobile_usage\nengine.execute(\"INSERT INTO european_healthcare SELECT * FROM mobile_usage\")\n", "labels": {"reads": [{"table": "mobile_usage", "columns": null}], "writes": [{"table": "european_healthcare", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO climate_finance_asia SELECT museumid, price_per_gram, saleamount FROM transportation_per_country WHERE museumid > 206\"\n", "labels": {"reads": [{"table": "transportation_per_country", "columns": ["museumid", "price_per_gram", "saleamount"]}], "writes": [{"table": "climate_finance_asia", "columns": ["museumid", "price_per_gram", "saleamount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"nursing_homes\")\nupsert_to_sink(df, \"circular_economy_initiatives\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "nursing_homes", "columns": null}], "writes": [{"table": "circular_economy_initiatives", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"catalog_structure\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "catalog_structure", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table seamounts --target-dir /tmp/land\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO facility SELECT fairtrade, course_type, fine_amount FROM tourdifferences WHERE fairtrade > 37\"\n", "labels": {"reads": [{"table": "tourdifferences", "columns": ["fairtrade", "course_type", "fine_amount"]}], "writes": [{"table": "facility", "columns": ["fairtrade", "course_type", "fine_amount"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table digital_trends --columns personnel,oil_production --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "digital_trends", "columns": ["personnel", "oil_production"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"school\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"concentrateprices\")\n", "labels": {"reads": [{"table": "school", "columns": null}], "writes": [{"table": "concentrateprices", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO volunteer_hours SELECT a.report, b.staff_address_id FROM schools a JOIN state_info b ON a.last_checkup_date = b.last_checkup_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "schools", "columns": null}, {"table": "state_info", "columns": null}], "writes": [{"table": "volunteer_hours", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO restaurant_revenue SELECT a.product_color, b.draft_pick_number FROM salesdata a JOIN courses b ON a.contextid = b.contextid\"\n", "labels": {"reads": [{"table": "salesdata", "columns": null}, {"table": "courses", "columns": null}], "writes": [{"table": "restaurant_revenue", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hotel_reviews\").toPandas()\ndf[[\"percentage_change\", \"nominee\"]].to_sql(\"visitor_statistics\", engine, index=False)\n", "labels": {"reads": [{"table": "hotel_reviews", "columns": null}], "writes": [{"table": "visitor_statistics", "columns": ["percentage_change", "nominee"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bridges (date_joined_staff, total) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bridges", "columns": ["date_joined_staff", "total"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT openingid, score FROM redundant_billing_data LIMIT 249\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO fertilizer SELECT contract_count, archeologist, approval_date FROM energy_consumption WHERE contract_count > 489\")\n", "labels": {"reads": [{"table": "redundant_billing_data", "columns": ["openingid", "score"]}, {"table": "energy_consumption", "columns": ["contract_count", "archeologist", "approval_date"]}], "writes": [{"table": "fertilizer", "columns": ["contract_count", "archeologist", "approval_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"development_hours\")\nsink_to_output(df, \"geological_survey\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "development_hours", "columns": null}], "writes": [{"table": "geological_survey", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM experts\", conn)\ndf.to_sql(\"wellbeing_program_participants\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "experts", "columns": null}], "writes": [{"table": "wellbeing_program_participants", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table electricvehicles --columns reservoir_id,stationname --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "electricvehicles", "columns": ["reservoir_id", "stationname"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO landfills (gamegenre, excavationid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "landfills", "columns": ["gamegenre", "excavationid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO transportation SELECT a.host_country, b.spacecraft FROM support_groups a JOIN ads.ads_payments b ON a.instid = b.instid\"\n", "labels": {"reads": [{"table": "support_groups", "columns": null}, {"table": "ads.ads_payments", "columns": null}], "writes": [{"table": "transportation", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO noise_pollution SELECT produceid, customer_first_name FROM ods.ods_device_log_delta WHERE produceid > 107\"\n", "labels": {"reads": [{"table": "ods.ods_device_log_delta", "columns": ["produceid", "customer_first_name"]}], "writes": [{"table": "noise_pollution", "columns": ["produceid", "customer_first_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bike_station_info\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"cars\")\n", "labels": {"reads": [{"table": "bike_station_info", "columns": null}], "writes": [{"table": "cars", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT therapy_session, response_type FROM rent_arrears\", engine)\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"stories\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "rent_arrears", "columns": ["therapy_session", "response_type"]}], "writes": [{"table": "stories", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO innovation_projects SELECT truck_details, manufacturer_name FROM co_ownership WHERE truck_details > 132\"\n", "labels": {"reads": [{"table": "co_ownership", "columns": ["truck_details", "manufacturer_name"]}], "writes": [{"table": "innovation_projects", "columns": ["truck_details", "manufacturer_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.distance > 232).all()\n# src table: satellite_deployment\nengine.execute(\"INSERT INTO community_development_projects SELECT * FROM satellite_deployment\")\n", "labels": {"reads": [{"table": "satellite_deployment", "columns": null}], "writes": [{"table": "community_development_projects", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT yearadded, sport_id FROM trainings LIMIT 245\")\nimport logging\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO electric_taxis SELECT startdate, vin, numpieces FROM department WHERE startdate > 204\")\n", "labels": {"reads": [{"table": "trainings", "columns": ["yearadded", "sport_id"]}, {"table": "department", "columns": ["startdate", "vin", "numpieces"]}], "writes": [{"table": "electric_taxis", "columns": ["startdate", "vin", "numpieces"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO soccer_teams SELECT mhw_id, catalog_name, warehouseid FROM city WHERE mhw_id > 133;\nEOF\n", "labels": {"reads": [{"table": "city", "columns": ["mhw_id", "catalog_name", "warehouseid"]}], "writes": [{"table": "soccer_teams", "columns": ["mhw_id", "catalog_name", "warehouseid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT home_city, donortype FROM tunnels LIMIT 46;\nINSERT INTO airport SELECT digital, other_details FROM member_data WHERE digital > 267;\nSQL\n", "labels": {"reads": [{"table": "tunnels", "columns": ["home_city", "donortype"]}, {"table": "member_data", "columns": ["digital", "other_details"]}], "writes": [{"table": "airport", "columns": ["digital", "other_details"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"auctions\").toPandas()\ndf[[\"regionname\", \"financial_wellbeing_score\"]].to_sql(\"check_ins\", engine, index=False)\n", "labels": {"reads": [{"table": "auctions", "columns": null}], "writes": [{"table": "check_ins", "columns": ["regionname", "financial_wellbeing_score"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM department_store_chain\", conn)\ndf.to_sql(\"cosmetics\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "department_store_chain", "columns": null}], "writes": [{"table": "cosmetics", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cultural_competency SELECT building_short_name, transaction_category FROM production_rare_earth_elements WHERE building_short_name > 15\"\n", "labels": {"reads": [{"table": "production_rare_earth_elements", "columns": ["building_short_name", "transaction_category"]}], "writes": [{"table": "cultural_competency", "columns": ["building_short_name", "transaction_category"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO weapons (unit_price, document_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "weapons", "columns": ["unit_price", "document_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_ad_interactions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"warehouses\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"assets\")\n", "labels": {"reads": [{"table": "warehouses", "columns": null}], "writes": [{"table": "assets", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"displaced_people\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"org_comms\")\n", "labels": {"reads": [{"table": "displaced_people", "columns": null}], "writes": [{"table": "org_comms", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO perpetrator SELECT 1\"\necho \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO electoral_register (launch_company, all_home) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "electoral_register", "columns": ["launch_company", "all_home"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_frame(ctx, \"manufacturingplants\")\nsave_to_store(df, \"clinics\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "manufacturingplants", "columns": null}], "writes": [{"table": "clinics", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_exposure_di SELECT password, partner FROM attendance WHERE password > 462\"], check=True)\n", "labels": {"reads": [{"table": "attendance", "columns": ["password", "partner"]}], "writes": [{"table": "stg.stg_exposure_di", "columns": ["password", "partner"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"game_sessions\")\nwrite_to_sink(df, \"renewable_projects\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "game_sessions", "columns": null}], "writes": [{"table": "renewable_projects", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.min_age > 415).all()\n# src table: ocean_temperatures\nengine.execute(\"INSERT INTO train_maintenance SELECT * FROM ocean_temperatures\")\n", "labels": {"reads": [{"table": "ocean_temperatures", "columns": null}], "writes": [{"table": "train_maintenance", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO waste_generation SELECT start, lesson_status_code, amount_funded, worker_count FROM request WHERE start > 491\")\n", "labels": {"reads": [{"table": "request", "columns": ["start", "lesson_status_code", "amount_funded", "worker_count"]}], "writes": [{"table": "waste_generation", "columns": ["start", "lesson_status_code", "amount_funded", "worker_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sales_by_quarter SELECT 1\"\necho \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_depths\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"county\")\n", "labels": {"reads": [{"table": "ocean_depths", "columns": null}], "writes": [{"table": "county", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO clothing_brands SELECT goals, mentalhealthscore FROM hospital_equipment WHERE goals > 469\")\n", "labels": {"reads": [{"table": "hospital_equipment", "columns": ["goals", "mentalhealthscore"]}], "writes": [{"table": "clothing_brands", "columns": ["goals", "mentalhealthscore"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table sustainableprojects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sustainableprojects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO artcollection SELECT num_tools, meal_id FROM student_tests_taken WHERE num_tools > 290\"\n", "labels": {"reads": [{"table": "student_tests_taken", "columns": ["num_tools", "meal_id"]}], "writes": [{"table": "artcollection", "columns": ["num_tools", "meal_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO consumer_preference SELECT * FROM legacy\ncur.execute(\"SELECT session_id, num_pallets FROM tree_habitat_associations LIMIT 438\")\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": ["session_id", "num_pallets"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT health_equity_metric_2, building_address FROM healthcare LIMIT 97\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "healthcare", "columns": ["health_equity_metric_2", "building_address"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mineral_extraction depends on measurements\ndbt run -s mineral_extraction --vars '{\"src\":\"measurements\"}'\n", "labels": {"reads": [{"table": "measurements", "columns": null}], "writes": [{"table": "mineral_extraction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"ods_sessions\")\nupsert_to_target(df, \"visualartprograms\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ods_sessions", "columns": null}], "writes": [{"table": "visualartprograms", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO companies SELECT a.creator, b.route_id FROM gradeconversion a JOIN catalogs b ON a.members = b.members\"\n", "labels": {"reads": [{"table": "gradeconversion", "columns": null}, {"table": "catalogs", "columns": null}], "writes": [{"table": "companies", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"clothingsales\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "clothingsales", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO criminalcases SELECT a.community_type, b.temporary_acting FROM environmental_impact_stats a JOIN ads.member_point b ON a.reported_by_staff_id = b.reported_by_staff_id\"\n", "labels": {"reads": [{"table": "environmental_impact_stats", "columns": null}, {"table": "ads.member_point", "columns": null}], "writes": [{"table": "criminalcases", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"branch\")\nsrc.write.insertInto(\"florida_conservation_initiatives\", overwrite=True)\n", "labels": {"reads": [{"table": "branch", "columns": null}], "writes": [{"table": "florida_conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO musicsales (units_owned, attendeeid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "musicsales", "columns": ["units_owned", "attendeeid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO monitoring_zones SELECT programoutcomeid, app_id FROM financial_capability WHERE programoutcomeid > 369\")\n", "labels": {"reads": [{"table": "financial_capability", "columns": ["programoutcomeid", "app_id"]}], "writes": [{"table": "monitoring_zones", "columns": ["programoutcomeid", "app_id"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"training\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "training", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.client_name > 445).all()\n# src table: heritage_sites_3\nengine.execute(\"INSERT INTO foodsafetyrecords SELECT * FROM heritage_sites_3\")\n", "labels": {"reads": [{"table": "heritage_sites_3", "columns": null}], "writes": [{"table": "foodsafetyrecords", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"machinery\")\nwrite_to_sink(df, \"branch\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "machinery", "columns": null}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO equipmentsales SELECT dname, shipment_id FROM mart.mart_vendors WHERE dname > 412\"], check=True)\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": ["dname", "shipment_id"]}], "writes": [{"table": "equipmentsales", "columns": ["dname", "shipment_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model space_programs depends on mentalhealthproviders\ndbt build --models space_programs --vars '{\"src\":\"mentalhealthproviders\"}'\n", "labels": {"reads": [{"table": "mentalhealthproviders", "columns": null}], "writes": [{"table": "space_programs", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO concert_sales SELECT certified, metric_id FROM user_likes WHERE certified > 112\"\n", "labels": {"reads": [{"table": "user_likes", "columns": ["certified", "metric_id"]}], "writes": [{"table": "concert_sales", "columns": ["certified", "metric_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dw.dw_payments_full\")\nsrc.write.insertInto(\"therapy_attendance\", overwrite=True)\n", "labels": {"reads": [{"table": "dw.dw_payments_full", "columns": null}], "writes": [{"table": "therapy_attendance", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO dw.dw_member_point_di SELECT subscriber_id, outcome, song_release_year, chemical_name FROM e_scooter_trips WHERE subscriber_id > 5\"\n", "labels": {"reads": [{"table": "e_scooter_trips", "columns": ["subscriber_id", "outcome", "song_release_year", "chemical_name"]}], "writes": [{"table": "dw.dw_member_point_di", "columns": ["subscriber_id", "outcome", "song_release_year", "chemical_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fans_merchandise_basketball SELECT a.hometown, b.allergytype FROM phone a JOIN discount_coupons b ON a.material_date = b.material_date\"\n", "labels": {"reads": [{"table": "phone", "columns": null}, {"table": "discount_coupons", "columns": null}], "writes": [{"table": "fans_merchandise_basketball", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO military_expenditure SELECT * FROM legacy\ncur.execute(\"SELECT assessmentdate, hardware_colours FROM recyclingratessouthamerica LIMIT 77\")\n", "labels": {"reads": [{"table": "recyclingratessouthamerica", "columns": ["assessmentdate", "hardware_colours"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.events SELECT roomtype, frameworkcountry, mine_name, iata FROM facility WHERE roomtype > 198\"\n", "labels": {"reads": [{"table": "facility", "columns": ["roomtype", "frameworkcountry", "mine_name", "iata"]}], "writes": [{"table": "ads.events", "columns": ["roomtype", "frameworkcountry", "mine_name", "iata"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads_refunds_full (platformid, enable_location_tracking) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads_refunds_full", "columns": ["platformid", "enable_location_tracking"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.dws_inventory_hourly (shippedcost, good_or_bad_customer) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.dws_inventory_hourly", "columns": ["shippedcost", "good_or_bad_customer"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO infra_diversification SELECT a.num_sessions, b.volume_id FROM bi.bi_events_full a JOIN stg_users_daily b ON a.call_time = b.call_time\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.bi_events_full", "columns": null}, {"table": "stg_users_daily", "columns": null}], "writes": [{"table": "infra_diversification", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ocean SELECT * FROM legacy\ncur.execute(\"SELECT event_attendance, floor_area_m2 FROM art_collection LIMIT 330\")\n", "labels": {"reads": [{"table": "art_collection", "columns": ["event_attendance", "floor_area_m2"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stars\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"crop_yield\")\n", "labels": {"reads": [{"table": "stars", "columns": null}], "writes": [{"table": "crop_yield", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT part_fault_id, practiceid FROM iron_ore_production LIMIT 177\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO economic_diversification_argentina SELECT form_type_code, employee_count, enable_location_tracking FROM indie_artists WHERE form_type_code > 67\")\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["part_fault_id", "practiceid"]}, {"table": "indie_artists", "columns": ["form_type_code", "employee_count", "enable_location_tracking"]}], "writes": [{"table": "economic_diversification_argentina", "columns": ["form_type_code", "employee_count", "enable_location_tracking"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"agencies\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"agro_regions\")\n", "labels": {"reads": [{"table": "agencies", "columns": null}], "writes": [{"table": "agro_regions", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO premises SELECT * FROM legacy\ncur.execute(\"SELECT inclusivehousing, participant_count FROM ca_menu_items LIMIT 264\")\n", "labels": {"reads": [{"table": "ca_menu_items", "columns": ["inclusivehousing", "participant_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM funding_records\", conn)\ndf.to_sql(\"ads.vendors_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "funding_records", "columns": null}], "writes": [{"table": "ads.vendors_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO health_equity_metrics SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws_events_di\")\nsrc.write.insertInto(\"sportsinfo\", overwrite=True)\n", "labels": {"reads": [{"table": "dws_events_di", "columns": null}], "writes": [{"table": "sportsinfo", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tvshows\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tvshows", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO eventparticipation SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ship_agent SELECT program_name, effort FROM milestones WHERE program_name > 16\"\n", "labels": {"reads": [{"table": "milestones", "columns": ["program_name", "effort"]}], "writes": [{"table": "ship_agent", "columns": ["program_name", "effort"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nhive -e \"INSERT INTO training_programs SELECT bdate, founder_identity, uses_vr, teamid FROM gameattendance WHERE bdate > 498\"\n", "labels": {"reads": [{"table": "gameattendance", "columns": ["bdate", "founder_identity", "uses_vr", "teamid"]}], "writes": [{"table": "training_programs", "columns": ["bdate", "founder_identity", "uses_vr", "teamid"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT state_province_county, check_in_date FROM carbon_emissions\", engine)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nimport logging\ndf.to_sql(\"climate_adaptation_re\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "carbon_emissions", "columns": ["state_province_county", "check_in_date"]}], "writes": [{"table": "climate_adaptation_re", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO pilot_record (claimid, date_of_completion) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "pilot_record", "columns": ["claimid", "date_of_completion"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ethicalaibudget SELECT working_year_starts, min_age, city FROM volunteerprograms WHERE working_year_starts > 454;\nEOF\n", "labels": {"reads": [{"table": "volunteerprograms", "columns": ["working_year_starts", "min_age", "city"]}], "writes": [{"table": "ethicalaibudget", "columns": ["working_year_starts", "min_age", "city"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO participation SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.exposure_df (username, dept_store_chain_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.exposure_df", "columns": ["username", "dept_store_chain_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO exit_strategies SELECT tour_id, review_date, tree_species, end_speed FROM cultural_competency WHERE tour_id > 84\"\n", "labels": {"reads": [{"table": "cultural_competency", "columns": ["tour_id", "review_date", "tree_species", "end_speed"]}], "writes": [{"table": "exit_strategies", "columns": ["tour_id", "review_date", "tree_species", "end_speed"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investmentsesg\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"marketing_budgets\")\n", "labels": {"reads": [{"table": "investmentsesg", "columns": null}], "writes": [{"table": "marketing_budgets", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO chemicals_annual SELECT * FROM legacy\ncur.execute(\"SELECT invoice_id, serviceid FROM contract_timeline LIMIT 14\")\n", "labels": {"reads": [{"table": "contract_timeline", "columns": ["invoice_id", "serviceid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods.ods_risk_score_df\"\n", "labels": {"reads": [{"table": "ods.ods_risk_score_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO assets_frameworks (budget_in_billions, average) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "assets_frameworks", "columns": ["budget_in_billions", "average"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO labor_cost SELECT dnumber, item_price FROM defense_projects WHERE dnumber > 418\"\n", "labels": {"reads": [{"table": "defense_projects", "columns": ["dnumber", "item_price"]}], "writes": [{"table": "labor_cost", "columns": ["dnumber", "item_price"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.content_type > 78).all()\n# src table: biotech_startups\nengine.execute(\"INSERT INTO tech_volunteers SELECT * FROM biotech_startups\")\n", "labels": {"reads": [{"table": "biotech_startups", "columns": null}], "writes": [{"table": "tech_volunteers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT investorgender, emp_fname FROM inst LIMIT 456;\nINSERT INTO smart_cities SELECT org_id, mentalhealthscore, cost FROM causes_insert_2 WHERE org_id > 13;\nSQL\n", "labels": {"reads": [{"table": "inst", "columns": ["investorgender", "emp_fname"]}, {"table": "causes_insert_2", "columns": ["org_id", "mentalhealthscore", "cost"]}], "writes": [{"table": "smart_cities", "columns": ["org_id", "mentalhealthscore", "cost"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM student_courses\"\n", "labels": {"reads": [{"table": "student_courses", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"ads.ads_inventory_df\")\ndump_to_sink(df, \"marine_life_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "ads.ads_inventory_df", "columns": null}], "writes": [{"table": "marine_life_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM stg.refunds_daily\", conn)\ndf.to_sql(\"urban_transportation\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "stg.refunds_daily", "columns": null}], "writes": [{"table": "urban_transportation", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT acc_regular_season, shelter_id FROM urban_transportation LIMIT 156\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO fish_farms SELECT protein_name, stat_type, incorporated_in, investmentid FROM recyclingrates WHERE protein_name > 163\")\n", "labels": {"reads": [{"table": "urban_transportation", "columns": ["acc_regular_season", "shelter_id"]}, {"table": "recyclingrates", "columns": ["protein_name", "stat_type", "incorporated_in", "investmentid"]}], "writes": [{"table": "fish_farms", "columns": ["protein_name", "stat_type", "incorporated_in", "investmentid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ocean_temperatures SELECT founder, consultations, follows_ethical_practices, num_rooms FROM dws.inventory_daily WHERE founder > 213\"\n", "labels": {"reads": [{"table": "dws.inventory_daily", "columns": ["founder", "consultations", "follows_ethical_practices", "num_rooms"]}], "writes": [{"table": "ocean_temperatures", "columns": ["founder", "consultations", "follows_ethical_practices", "num_rooms"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.research_name > 29).all()\n# src table: retail_workers_union\nengine.execute(\"INSERT INTO container_ships SELECT * FROM retail_workers_union\")\n", "labels": {"reads": [{"table": "retail_workers_union", "columns": null}], "writes": [{"table": "container_ships", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table round --columns heritage_site_id,last_inspection_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "round", "columns": ["heritage_site_id", "last_inspection_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dw.events_hourly SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO agro_regions SELECT * FROM legacy\ncur.execute(\"SELECT personal_name, technique FROM contract_negotiations_un LIMIT 451\")\n", "labels": {"reads": [{"table": "contract_negotiations_un", "columns": ["personal_name", "technique"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT sustainability_id, artifacttype FROM ticket_sales LIMIT 126\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO ads.ads_cart_item_hourly SELECT shelter_name, campaign_name, city_town FROM marine_species_observations WHERE shelter_name > 275\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": ["sustainability_id", "artifacttype"]}, {"table": "marine_species_observations", "columns": ["shelter_name", "campaign_name", "city_town"]}], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": ["shelter_name", "campaign_name", "city_town"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table trust --target-dir /tmp/land\n", "labels": {"reads": [{"table": "trust", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"esports_teams\")\nsave_to_store(df, \"arctictemperature\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "esports_teams", "columns": null}], "writes": [{"table": "arctictemperature", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dispensarysales SELECT price_in_euros, education_id, negative FROM highways WHERE price_in_euros > 307\"\n", "labels": {"reads": [{"table": "highways", "columns": ["price_in_euros", "education_id", "negative"]}], "writes": [{"table": "dispensarysales", "columns": ["price_in_euros", "education_id", "negative"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO foodaid SELECT healthcareid, date_assigned_to FROM bookings WHERE healthcareid > 33\")\n", "labels": {"reads": [{"table": "bookings", "columns": ["healthcareid", "date_assigned_to"]}], "writes": [{"table": "foodaid", "columns": ["healthcareid", "date_assigned_to"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"rnd_budget\")\nsrc.write.insertInto(\"singer_in_concert\", overwrite=True)\n", "labels": {"reads": [{"table": "rnd_budget", "columns": null}], "writes": [{"table": "singer_in_concert", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"clinics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"stg.stg_products_delta\")\n", "labels": {"reads": [{"table": "clinics", "columns": null}], "writes": [{"table": "stg.stg_products_delta", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM cultural_competency_training\", conn)\ndf.to_sql(\"player_sessions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "cultural_competency_training", "columns": null}], "writes": [{"table": "player_sessions", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"seamounts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"supplychainemployees\")\n", "labels": {"reads": [{"table": "seamounts", "columns": null}], "writes": [{"table": "supplychainemployees", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO textile_sourcing SELECT a.fault_log_entry_datetime, b.recipient FROM team a JOIN space_missions b ON a.mining_operation = b.mining_operation\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "team", "columns": null}, {"table": "space_missions", "columns": null}], "writes": [{"table": "textile_sourcing", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO concert_sales SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO mineral_extraction_us SELECT tourists, protein_name, white FROM candidate_assessments WHERE tourists > 480\")\n", "labels": {"reads": [{"table": "candidate_assessments", "columns": ["tourists", "protein_name", "white"]}], "writes": [{"table": "mineral_extraction_us", "columns": ["tourists", "protein_name", "white"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO volunteerprograms SELECT reviewscore, claim_outcome_code FROM container WHERE reviewscore > 32\"\n", "labels": {"reads": [{"table": "container", "columns": ["reviewscore", "claim_outcome_code"]}], "writes": [{"table": "volunteerprograms", "columns": ["reviewscore", "claim_outcome_code"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"funding_rounds\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"underwater_trenches\")\n", "labels": {"reads": [{"table": "funding_rounds", "columns": null}], "writes": [{"table": "underwater_trenches", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO tracks (vegetable, co2_reduction_tons) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "tracks", "columns": ["vegetable", "co2_reduction_tons"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"investors\")\nsrc.write.insertInto(\"species_forests\", overwrite=True)\n", "labels": {"reads": [{"table": "investors", "columns": null}], "writes": [{"table": "species_forests", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT humidity, vendor FROM dw.events_hourly LIMIT 202\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO funding_rounds SELECT founded, gametype, visitors, exhibitions FROM domesticconferences WHERE founded > 20\")\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": ["humidity", "vendor"]}, {"table": "domesticconferences", "columns": ["founded", "gametype", "visitors", "exhibitions"]}], "writes": [{"table": "funding_rounds", "columns": ["founded", "gametype", "visitors", "exhibitions"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO affordablehousing SELECT party_name, ad_id FROM stg.refunds_hourly WHERE party_name > 298\"], check=True)\n", "labels": {"reads": [{"table": "stg.refunds_hourly", "columns": ["party_name", "ad_id"]}], "writes": [{"table": "affordablehousing", "columns": ["party_name", "ad_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT month, price_in_dollars FROM buildings LIMIT 32\")\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nspark.sql(\"INSERT INTO ref_product_categories SELECT contract_start_date, spf_level, shipped_date FROM animal_rehab WHERE contract_start_date > 136\")\n", "labels": {"reads": [{"table": "buildings", "columns": ["month", "price_in_dollars"]}, {"table": "animal_rehab", "columns": ["contract_start_date", "spf_level", "shipped_date"]}], "writes": [{"table": "ref_product_categories", "columns": ["contract_start_date", "spf_level", "shipped_date"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.average > 185).all()\n# src table: programs\nengine.execute(\"INSERT INTO architect SELECT * FROM programs\")\n", "labels": {"reads": [{"table": "programs", "columns": null}], "writes": [{"table": "architect", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO captain (threats, song_year) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "captain", "columns": ["threats", "song_year"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM sustainability_metrics\", conn)\ndf.to_sql(\"impact_asia\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "sustainability_metrics", "columns": null}], "writes": [{"table": "impact_asia", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM movie_ratings\"\n", "labels": {"reads": [{"table": "movie_ratings", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ods_cart_item_df depends on atlantic_ocean\ndbt build --select ods_cart_item_df --vars '{\"source_table\":\"atlantic_ocean\"}'\n", "labels": {"reads": [{"table": "atlantic_ocean", "columns": null}], "writes": [{"table": "ods_cart_item_df", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO nursing_homes SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.dw_orders_hourly (energy_source, restaurant) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.dw_orders_hourly", "columns": ["energy_source", "restaurant"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model facility depends on tourism_activities\ndbt run --models facility --vars 'source: tourism_activities'\n", "labels": {"reads": [{"table": "tourism_activities", "columns": null}], "writes": [{"table": "facility", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dapps SELECT shipped_date, payment_type_code, model_id, article_id FROM lives_in WHERE shipped_date > 487\"\n", "labels": {"reads": [{"table": "lives_in", "columns": ["shipped_date", "payment_type_code", "model_id", "article_id"]}], "writes": [{"table": "dapps", "columns": ["shipped_date", "payment_type_code", "model_id", "article_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"passenger_trips\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tennis_players\")\n", "labels": {"reads": [{"table": "passenger_trips", "columns": null}], "writes": [{"table": "tennis_players", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_payment_made, meter_100 FROM all_documents LIMIT 52\")\nrows = cur.fetchall()\nimport logging\n", "labels": {"reads": [{"table": "all_documents", "columns": ["date_payment_made", "meter_100"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"socially_responsible_lending\")\nsrc.write.insertInto(\"medical_professionals\", overwrite=True)\n", "labels": {"reads": [{"table": "socially_responsible_lending", "columns": null}], "writes": [{"table": "medical_professionals", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO public_transportation_sydney (equipment, num_sessions) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "public_transportation_sydney", "columns": ["equipment", "num_sessions"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO spacemissions (enable_location_tracking, model) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "spacemissions", "columns": ["enable_location_tracking", "model"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO mineral_extraction SELECT labor_id, lastdonationdate, permitdate FROM station_company WHERE labor_id > 248\"\n", "labels": {"reads": [{"table": "station_company", "columns": ["labor_id", "lastdonationdate", "permitdate"]}], "writes": [{"table": "mineral_extraction", "columns": ["labor_id", "lastdonationdate", "permitdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO settlements SELECT a.ll_hours, b.staff_first_name FROM ads.ads_risk_score_hourly a JOIN material b ON a.damage_millions_usd = b.damage_millions_usd\"\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": null}, {"table": "material", "columns": null}], "writes": [{"table": "settlements", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fund_investments SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nsql = \"INSERT INTO bi.device_log SELECT a.group_name, b.launch FROM artwork_styles a JOIN timber_sales b ON a.status_code = b.status_code\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "artwork_styles", "columns": null}, {"table": "timber_sales", "columns": null}], "writes": [{"table": "bi.device_log", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT assessmentdate, site_name FROM professionals LIMIT 437;\nINSERT INTO view_unit_status SELECT deliverydate, mentalhealthscore FROM circulation_history WHERE deliverydate > 20;\nSQL\n", "labels": {"reads": [{"table": "professionals", "columns": ["assessmentdate", "site_name"]}, {"table": "circulation_history", "columns": ["deliverydate", "mentalhealthscore"]}], "writes": [{"table": "view_unit_status", "columns": ["deliverydate", "mentalhealthscore"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO cases SELECT 1\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO round SELECT * FROM legacy\ncur.execute(\"SELECT policy_holder_id, material_id FROM underwater_trenches LIMIT 69\")\n", "labels": {"reads": [{"table": "underwater_trenches", "columns": ["policy_holder_id", "material_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table program_history --columns event_name,conferenceid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "program_history", "columns": ["event_name", "conferenceid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model labor_hours depends on ads.ads_users_hourly\ndbt run --select labor_hours --vars 'source: ads.ads_users_hourly'\n", "labels": {"reads": [{"table": "ads.ads_users_hourly", "columns": null}], "writes": [{"table": "labor_hours", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO justice_schemas.legal_tech_providers SELECT a.medical_risk, b.num_beds FROM communitydevelopment a JOIN diversity_metrics b ON a.dnumber = b.dnumber\"\n", "labels": {"reads": [{"table": "communitydevelopment", "columns": null}, {"table": "diversity_metrics", "columns": null}], "writes": [{"table": "justice_schemas.legal_tech_providers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"spacecrafts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"agricultural_innovations\")\n", "labels": {"reads": [{"table": "spacecrafts", "columns": null}], "writes": [{"table": "agricultural_innovations", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO global_sales_2022 SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO excavation_sites (target_name, specialty) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "excavation_sites", "columns": ["target_name", "specialty"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table impact_asia --columns customername,amenity_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "impact_asia", "columns": ["customername", "amenity_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT tour_name, publication_id FROM water_distribution LIMIT 51\")\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ref_attraction_types SELECT grant_end_date, contact_number, num_of_factories, hireyear FROM nutritionfacts WHERE grant_end_date > 278\")\n", "labels": {"reads": [{"table": "water_distribution", "columns": ["tour_name", "publication_id"]}, {"table": "nutritionfacts", "columns": ["grant_end_date", "contact_number", "num_of_factories", "hireyear"]}], "writes": [{"table": "ref_attraction_types", "columns": ["grant_end_date", "contact_number", "num_of_factories", "hireyear"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO fair_trade_brands SELECT a.snatch, b.review_text FROM mart_shipments_full a JOIN tree_species b ON a.shop_details = b.shop_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": null}, {"table": "tree_species", "columns": null}], "writes": [{"table": "fair_trade_brands", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO cosmetics SELECT subscriber_id, other_characteristic_details, method_name, node_id FROM cycling WHERE subscriber_id > 33\"\n", "labels": {"reads": [{"table": "cycling", "columns": ["subscriber_id", "other_characteristic_details", "method_name", "node_id"]}], "writes": [{"table": "cosmetics", "columns": ["subscriber_id", "other_characteristic_details", "method_name", "node_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT arrival, community_center_id FROM artsales LIMIT 45\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "artsales", "columns": ["arrival", "community_center_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table trenches --target-dir /tmp/land\n", "labels": {"reads": [{"table": "trenches", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nspark.sql(\"INSERT INTO performers SELECT typical_selling_price, district, measurement, wage FROM trainings WHERE typical_selling_price > 88\")\n", "labels": {"reads": [{"table": "trainings", "columns": ["typical_selling_price", "district", "measurement", "wage"]}], "writes": [{"table": "performers", "columns": ["typical_selling_price", "district", "measurement", "wage"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table tv_shows_genre --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cb_year, aid_name FROM problem_log LIMIT 43\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "problem_log", "columns": ["cb_year", "aid_name"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model dws_users_hourly depends on contract_transactions\ndbt build -s dws_users_hourly --vars '{\"src\":\"contract_transactions\"}'\n", "labels": {"reads": [{"table": "contract_transactions", "columns": null}], "writes": [{"table": "dws_users_hourly", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"military_equipment_maintenance\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"navalvessels\")\n", "labels": {"reads": [{"table": "military_equipment_maintenance", "columns": null}], "writes": [{"table": "navalvessels", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tours\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"defense_contracts\")\n", "labels": {"reads": [{"table": "tours", "columns": null}], "writes": [{"table": "defense_contracts", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ticketsales SELECT purchase_transaction_id, operationid FROM marine_life_data WHERE purchase_transaction_id > 384\"\n", "labels": {"reads": [{"table": "marine_life_data", "columns": ["purchase_transaction_id", "operationid"]}], "writes": [{"table": "ticketsales", "columns": ["purchase_transaction_id", "operationid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nhive -e \"INSERT INTO harvest_permits SELECT incident_name, missions FROM party_host WHERE incident_name > 242\"\n", "labels": {"reads": [{"table": "party_host", "columns": ["incident_name", "missions"]}], "writes": [{"table": "harvest_permits", "columns": ["incident_name", "missions"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table lawyers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "lawyers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"classicgame\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM project_timeline\", conn)\ndf.to_sql(\"water_conservation_brazil\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "project_timeline", "columns": null}], "writes": [{"table": "water_conservation_brazil", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO healthydelights SELECT warehousename, retailer_name FROM virtual_tourism WHERE warehousename > 413\"], check=True)\n", "labels": {"reads": [{"table": "virtual_tourism", "columns": ["warehousename", "retailer_name"]}], "writes": [{"table": "healthydelights", "columns": ["warehousename", "retailer_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainable_building SELECT route_short_name, cmi_cross_ref_id FROM regular_order_products WHERE route_short_name > 25\"\n", "labels": {"reads": [{"table": "regular_order_products", "columns": ["route_short_name", "cmi_cross_ref_id"]}], "writes": [{"table": "sustainable_building", "columns": ["route_short_name", "cmi_cross_ref_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stories\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"emissions\")\n", "labels": {"reads": [{"table": "stories", "columns": null}], "writes": [{"table": "emissions", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO community_centers SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO rebounds SELECT route_short_name, project_details, neighborhoodid FROM bus_fare_collection WHERE route_short_name > 422\"], check=True)\n", "labels": {"reads": [{"table": "bus_fare_collection", "columns": ["route_short_name", "project_details", "neighborhoodid"]}], "writes": [{"table": "rebounds", "columns": ["route_short_name", "project_details", "neighborhoodid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO fare_collection SELECT a.incident_region, b.is_male FROM digital_trends a JOIN features b ON a.defendant_id = b.defendant_id\"\n", "labels": {"reads": [{"table": "digital_trends", "columns": null}, {"table": "features", "columns": null}], "writes": [{"table": "fare_collection", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dw.events_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_basins\")\n", "labels": {"reads": [{"table": "dw.events_hourly", "columns": null}], "writes": [{"table": "ocean_basins", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO experts SELECT 1\"\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO inclusive_housing SELECT a.width, b.program_category FROM light_rail_lines a JOIN production_costs b ON a.playername = b.playername\"\n", "labels": {"reads": [{"table": "light_rail_lines", "columns": null}, {"table": "production_costs", "columns": null}], "writes": [{"table": "inclusive_housing", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT allergytype, courtname FROM commodity_prices LIMIT 123\")\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO recreation_centers SELECT yield_per_acre, spacecraft_id, reported_date, running_time FROM stg.stg_campaigns_hourly WHERE yield_per_acre > 273\")\n", "labels": {"reads": [{"table": "commodity_prices", "columns": ["allergytype", "courtname"]}, {"table": "stg.stg_campaigns_hourly", "columns": ["yield_per_acre", "spacecraft_id", "reported_date", "running_time"]}], "writes": [{"table": "recreation_centers", "columns": ["yield_per_acre", "spacecraft_id", "reported_date", "running_time"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table completed_training --columns farmid,medical_condition --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "completed_training", "columns": ["farmid", "medical_condition"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sales_quarterly\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"party_host\")\n", "labels": {"reads": [{"table": "sales_quarterly", "columns": null}], "writes": [{"table": "party_host", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT dish_name, labor_practice FROM dispensarysales LIMIT 485\")\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nspark.sql(\"INSERT INTO food_safety_inspections SELECT attribute_id, representative_name, organisation_id, strategy FROM pipelines_us_canada WHERE attribute_id > 466\")\n", "labels": {"reads": [{"table": "dispensarysales", "columns": ["dish_name", "labor_practice"]}, {"table": "pipelines_us_canada", "columns": ["attribute_id", "representative_name", "organisation_id", "strategy"]}], "writes": [{"table": "food_safety_inspections", "columns": ["attribute_id", "representative_name", "organisation_id", "strategy"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO space_missions SELECT a.word_count, b.annual_entry_exit FROM surveylocations a JOIN policyimpact b ON a.adoption_date = b.adoption_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "surveylocations", "columns": null}, {"table": "policyimpact", "columns": null}], "writes": [{"table": "space_missions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT agency_id, amount_payment FROM emergency_calls LIMIT 479\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "emergency_calls", "columns": ["agency_id", "amount_payment"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.bi_risk_score_full SELECT workeridentity, founded_year, supply_volume FROM forestry_practices WHERE workeridentity > 219\"\n", "labels": {"reads": [{"table": "forestry_practices", "columns": ["workeridentity", "founded_year", "supply_volume"]}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["workeridentity", "founded_year", "supply_volume"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads_sessions_di SELECT project_category, museumid, programoutcomeid, biome_id FROM tracks WHERE project_category > 251\"\n", "labels": {"reads": [{"table": "tracks", "columns": ["project_category", "museumid", "programoutcomeid", "biome_id"]}], "writes": [{"table": "ads_sessions_di", "columns": ["project_category", "museumid", "programoutcomeid", "biome_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO housing_investments SELECT deaths, budgetid, census_ranking, student_capacity FROM human_resources WHERE deaths > 258\"\n", "labels": {"reads": [{"table": "human_resources", "columns": ["deaths", "budgetid", "census_ranking", "student_capacity"]}], "writes": [{"table": "housing_investments", "columns": ["deaths", "budgetid", "census_ranking", "student_capacity"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO initiatives SELECT * FROM legacy\ncur.execute(\"SELECT sensor_id, winery FROM bi.products_daily LIMIT 103\")\n", "labels": {"reads": [{"table": "bi.products_daily", "columns": ["sensor_id", "winery"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT date_of_latest_revision, brand_mentioned FROM customers_policies LIMIT 167\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "customers_policies", "columns": ["date_of_latest_revision", "brand_mentioned"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model humanitarian_aid depends on marine_species_indian\ndbt build --select humanitarian_aid --vars '{\"src\":\"marine_species_indian\"}'\n", "labels": {"reads": [{"table": "marine_species_indian", "columns": null}], "writes": [{"table": "humanitarian_aid", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO inspectiondata SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM smartcitycosts\", conn)\ndf.to_sql(\"project_timelines\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "smartcitycosts", "columns": null}], "writes": [{"table": "project_timelines", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO funding_rounds SELECT personnelbranch, matchdate, mean_sea_level_pressure_inches FROM outcomes WHERE personnelbranch > 281\"\n", "labels": {"reads": [{"table": "outcomes", "columns": ["personnelbranch", "matchdate", "mean_sea_level_pressure_inches"]}], "writes": [{"table": "funding_rounds", "columns": ["personnelbranch", "matchdate", "mean_sea_level_pressure_inches"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM policyanalysis\", conn)\ndf.to_sql(\"degrees\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "policyanalysis", "columns": null}], "writes": [{"table": "degrees", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_payments_di\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ads_payments_di", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO electric_vehicles SELECT ironid, opening_hours, start_station_id FROM stg.coupon_use WHERE ironid > 2\"\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": ["ironid", "opening_hours", "start_station_id"]}], "writes": [{"table": "electric_vehicles", "columns": ["ironid", "opening_hours", "start_station_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO trees SELECT town_city, subscribe_date, profits_billion, annual_entry_exit FROM playerscores WHERE town_city > 453\"\n", "labels": {"reads": [{"table": "playerscores", "columns": ["town_city", "subscribe_date", "profits_billion", "annual_entry_exit"]}], "writes": [{"table": "trees", "columns": ["town_city", "subscribe_date", "profits_billion", "annual_entry_exit"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_input(ctx, \"staff\")\nsink_to_warehouse(df, \"user_workouts_march\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "staff", "columns": null}], "writes": [{"table": "user_workouts_march", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table engineer_visits --target-dir /tmp/land\n", "labels": {"reads": [{"table": "engineer_visits", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO midwest_materials SELECT num_hotels, analysis_date, passengers FROM grad_students WHERE num_hotels > 199\"\n", "labels": {"reads": [{"table": "grad_students", "columns": ["num_hotels", "analysis_date", "passengers"]}], "writes": [{"table": "midwest_materials", "columns": ["num_hotels", "analysis_date", "passengers"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model chemical_concentration depends on bi.bi_sessions_hourly\ndbt run --models chemical_concentration --vars 'source: bi.bi_sessions_hourly'\n", "labels": {"reads": [{"table": "bi.bi_sessions_hourly", "columns": null}], "writes": [{"table": "chemical_concentration", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT meter_300, opening_hours FROM customer_policies LIMIT 473\")\nimport logging\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO site SELECT census_ranking, license_id FROM band WHERE census_ranking > 489\")\n", "labels": {"reads": [{"table": "customer_policies", "columns": ["meter_300", "opening_hours"]}, {"table": "band", "columns": ["census_ranking", "license_id"]}], "writes": [{"table": "site", "columns": ["census_ranking", "license_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM aus_wellbeing\", conn)\ndf.to_sql(\"dwd.dwd_events_delta\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "aus_wellbeing", "columns": null}], "writes": [{"table": "dwd.dwd_events_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT premise_id, shipping_agent_code FROM spacecraft LIMIT 304;\nINSERT INTO chemicals_annual SELECT hoursspent, enrollment, donationamount, cargo_id FROM body_builder WHERE hoursspent > 173;\nSQL\n", "labels": {"reads": [{"table": "spacecraft", "columns": ["premise_id", "shipping_agent_code"]}, {"table": "body_builder", "columns": ["hoursspent", "enrollment", "donationamount", "cargo_id"]}], "writes": [{"table": "chemicals_annual", "columns": ["hoursspent", "enrollment", "donationamount", "cargo_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT health_equity_metric_3, reason FROM agency_satellites\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"dwd_sessions_hourly\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "agency_satellites", "columns": ["health_equity_metric_3", "reason"]}], "writes": [{"table": "dwd_sessions_hourly", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO enrolled_in SELECT 1\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO ads.ads_orders SELECT a.jobtitle, b.art_movement FROM papers a JOIN financial_transactions b ON a.missionid = b.missionid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "papers", "columns": null}, {"table": "financial_transactions", "columns": null}], "writes": [{"table": "ads.ads_orders", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT median_home_value, publication_id FROM conditions LIMIT 313;\nINSERT INTO payments SELECT altitude, volunteer_name, cuisine_name FROM order_items WHERE altitude > 401;\nSQL\n", "labels": {"reads": [{"table": "conditions", "columns": ["median_home_value", "publication_id"]}, {"table": "order_items", "columns": ["altitude", "volunteer_name", "cuisine_name"]}], "writes": [{"table": "payments", "columns": ["altitude", "volunteer_name", "cuisine_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ref_shipping_agents (playdate, advocate_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ref_shipping_agents", "columns": ["playdate", "advocate_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO climate_communication_projects (transaction_value, price_in_dollar) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "climate_communication_projects", "columns": ["transaction_value", "price_in_dollar"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table bi_campaigns_delta --columns birth_country,grantamount --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "bi_campaigns_delta", "columns": ["birth_country", "grantamount"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_events_di\").toPandas()\ndf[[\"open_year\", \"investment_amount\"]].to_sql(\"ads.ads_clicks_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_events_di", "columns": null}], "writes": [{"table": "ads.ads_clicks_delta", "columns": ["open_year", "investment_amount"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"electricvehicles\").toPandas()\ndf[[\"policytype\", \"building_full_name\"]].to_sql(\"dwd.dwd_exposure_df\", engine, index=False)\n", "labels": {"reads": [{"table": "electricvehicles", "columns": null}], "writes": [{"table": "dwd.dwd_exposure_df", "columns": ["policytype", "building_full_name"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"dws.dws_inventory_di\")\nsrc.write.insertInto(\"investors\", overwrite=True)\n", "labels": {"reads": [{"table": "dws.dws_inventory_di", "columns": null}], "writes": [{"table": "investors", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO dwd.dwd_member_point_di SELECT practiceid, num_virtual_tours, number_thousands FROM clothing_brands WHERE practiceid > 262\"\n", "labels": {"reads": [{"table": "clothing_brands", "columns": ["practiceid", "num_virtual_tours", "number_thousands"]}], "writes": [{"table": "dwd.dwd_member_point_di", "columns": ["practiceid", "num_virtual_tours", "number_thousands"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_input(ctx, \"road_construction\")\nsink_to_target(df, \"stg.stg_risk_score_df\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "road_construction", "columns": null}], "writes": [{"table": "stg.stg_risk_score_df", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT sanctuary, city FROM green_projects LIMIT 394;\nINSERT INTO companies SELECT region_id, dispensary_name, color_code FROM community_engagement WHERE region_id > 92;\nSQL\n", "labels": {"reads": [{"table": "green_projects", "columns": ["sanctuary", "city"]}, {"table": "community_engagement", "columns": ["region_id", "dispensary_name", "color_code"]}], "writes": [{"table": "companies", "columns": ["region_id", "dispensary_name", "color_code"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nimport logging\nsql = \"INSERT INTO container SELECT a.added_date, b.actual_order_id FROM recalls a JOIN recycling_rates_oceania b ON a.age = b.age\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "recalls", "columns": null}, {"table": "recycling_rates_oceania", "columns": null}], "writes": [{"table": "container", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO indian_ocean_fishingvessels SELECT product_price, role_code, indigenous FROM dw.dw_member_point_hourly WHERE product_price > 371\"\n", "labels": {"reads": [{"table": "dw.dw_member_point_hourly", "columns": ["product_price", "role_code", "indigenous"]}], "writes": [{"table": "indian_ocean_fishingvessels", "columns": ["product_price", "role_code", "indigenous"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT community_size, artworkyear FROM stateinfrastructure\", engine)\nimport logging\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"nba\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "stateinfrastructure", "columns": ["community_size", "artworkyear"]}], "writes": [{"table": "nba", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO goals SELECT attorney_id, task FROM donations2022 WHERE attorney_id > 476\"\n", "labels": {"reads": [{"table": "donations2022", "columns": ["attorney_id", "task"]}], "writes": [{"table": "goals", "columns": ["attorney_id", "task"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO assets SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO supportservices SELECT task_details, age FROM program_history WHERE task_details > 272;\nEOF\n", "labels": {"reads": [{"table": "program_history", "columns": ["task_details", "age"]}], "writes": [{"table": "supportservices", "columns": ["task_details", "age"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model maintenancerequests depends on infrastructureprojects\ndbt run -s maintenancerequests --vars 'source: infrastructureprojects'\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": null}], "writes": [{"table": "maintenancerequests", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"energy_efficiency_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"eu_data_usage\")\n", "labels": {"reads": [{"table": "energy_efficiency_projects", "columns": null}], "writes": [{"table": "eu_data_usage", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ods_products_delta --columns rental_date,enroll_grade --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ods_products_delta", "columns": ["rental_date", "enroll_grade"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model inventory_daily depends on ods.ods_campaigns_hourly\ndbt build --select inventory_daily --vars '{\"src\":\"ods.ods_campaigns_hourly\"}'\n", "labels": {"reads": [{"table": "ods.ods_campaigns_hourly", "columns": null}], "writes": [{"table": "inventory_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO safetytestingcounts SELECT batting_average, tourist_attraction_id, velocity FROM drills WHERE batting_average > 152\")\n", "labels": {"reads": [{"table": "drills", "columns": ["batting_average", "tourist_attraction_id", "velocity"]}], "writes": [{"table": "safetytestingcounts", "columns": ["batting_average", "tourist_attraction_id", "velocity"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table research_vessels --columns ranking,founder_group --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "research_vessels", "columns": ["ranking", "founder_group"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT section_title, allocation_type FROM facility_production LIMIT 176\")\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO reo_production SELECT healthequitymetricscore, practice_id, online_dispute_resolution, max_wind_speed_mph FROM trenches WHERE healthequitymetricscore > 238\")\n", "labels": {"reads": [{"table": "facility_production", "columns": ["section_title", "allocation_type"]}, {"table": "trenches", "columns": ["healthequitymetricscore", "practice_id", "online_dispute_resolution", "max_wind_speed_mph"]}], "writes": [{"table": "reo_production", "columns": ["healthequitymetricscore", "practice_id", "online_dispute_resolution", "max_wind_speed_mph"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nspark.sql(\"INSERT INTO furniture SELECT source, album_id, products_this_year FROM total_capacity WHERE source > 159\")\n", "labels": {"reads": [{"table": "total_capacity", "columns": ["source", "album_id", "products_this_year"]}], "writes": [{"table": "furniture", "columns": ["source", "album_id", "products_this_year"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT representative_name, attendanceid FROM railway LIMIT 489\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "railway", "columns": ["representative_name", "attendanceid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.practiceid > 185).all()\n# src table: employeedata\nengine.execute(\"INSERT INTO disabilityadvocacy SELECT * FROM employeedata\")\n", "labels": {"reads": [{"table": "employeedata", "columns": null}], "writes": [{"table": "disabilityadvocacy", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table mart.mart_coupon_use_full --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_full", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT founder_count, enddate FROM player_sessions LIMIT 464\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO customer_master_index SELECT visit_date, store_email_address, mhw_id FROM rating WHERE visit_date > 181\")\n", "labels": {"reads": [{"table": "player_sessions", "columns": ["founder_count", "enddate"]}, {"table": "rating", "columns": ["visit_date", "store_email_address", "mhw_id"]}], "writes": [{"table": "customer_master_index", "columns": ["visit_date", "store_email_address", "mhw_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO energy_efficiency_projects SELECT fanid, maxoccupancy, market_value_billion, building_address FROM iron_ore_production WHERE fanid > 296\"\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": ["fanid", "maxoccupancy", "market_value_billion", "building_address"]}], "writes": [{"table": "energy_efficiency_projects", "columns": ["fanid", "maxoccupancy", "market_value_billion", "building_address"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO attack_outcomes (assets_billion, film_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "attack_outcomes", "columns": ["assets_billion", "film_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"gene\")\nexport_to_target(df, \"num_employees\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "gene", "columns": null}], "writes": [{"table": "num_employees", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"service_budget\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "service_budget", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"defense_project_timelines\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"donationprograms\")\n", "labels": {"reads": [{"table": "defense_project_timelines", "columns": null}], "writes": [{"table": "donationprograms", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sustainable_practices SELECT end_station_name, satellite_name FROM legalaidrequests WHERE end_station_name > 326;\nEOF\n", "labels": {"reads": [{"table": "legalaidrequests", "columns": ["end_station_name", "satellite_name"]}], "writes": [{"table": "sustainable_practices", "columns": ["end_station_name", "satellite_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table farmers --target-dir /tmp/land\n", "labels": {"reads": [{"table": "farmers", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"trends_2022\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"chemicalbatches\")\n", "labels": {"reads": [{"table": "trends_2022", "columns": null}], "writes": [{"table": "chemicalbatches", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_clinics\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"transportation_union\")\n", "labels": {"reads": [{"table": "rural_clinics", "columns": null}], "writes": [{"table": "transportation_union", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO creativeais SELECT is_recycled, tonnage FROM investor_activities WHERE is_recycled > 90\")\n", "labels": {"reads": [{"table": "investor_activities", "columns": ["is_recycled", "tonnage"]}], "writes": [{"table": "creativeais", "columns": ["is_recycled", "tonnage"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO traffic_violations SELECT last_maintenance, fabrictype, producerid, hours_spent FROM suburbs WHERE last_maintenance > 488\"\n", "labels": {"reads": [{"table": "suburbs", "columns": ["last_maintenance", "fabrictype", "producerid", "hours_spent"]}], "writes": [{"table": "traffic_violations", "columns": ["last_maintenance", "fabrictype", "producerid", "hours_spent"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"measurement\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "measurement", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM workforce\", conn)\ndf.to_sql(\"support_programs\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "workforce", "columns": null}], "writes": [{"table": "support_programs", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO paris_train SELECT 1\"\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_table(ctx, \"mart.mart_vendors\")\npush_to_warehouse(df, \"ocean_acidification\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mart.mart_vendors", "columns": null}], "writes": [{"table": "ocean_acidification", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"artifacts\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"party_host\")\n", "labels": {"reads": [{"table": "artifacts", "columns": null}], "writes": [{"table": "party_host", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO company_info (bike_id, dock_count) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "company_info", "columns": ["bike_id", "dock_count"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table shared_escooters --target-dir /tmp/land\n", "labels": {"reads": [{"table": "shared_escooters", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO southchinasea.wells (location, crs_credit) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "southchinasea.wells", "columns": ["location", "crs_credit"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"hosting_city\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "hosting_city", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table communitypolicingcenters --target-dir /tmp/land\n", "labels": {"reads": [{"table": "communitypolicingcenters", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO geological_survey (number_of_sightings, pid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "geological_survey", "columns": ["number_of_sightings", "pid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO enzyme SELECT international_passengers, dispensary_name, is_hybrid FROM animals WHERE international_passengers > 195\")\n", "labels": {"reads": [{"table": "animals", "columns": ["international_passengers", "dispensary_name", "is_hybrid"]}], "writes": [{"table": "enzyme", "columns": ["international_passengers", "dispensary_name", "is_hybrid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table categories --columns grant_end_date,role_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "categories", "columns": ["grant_end_date", "role_code"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO ods.sessions SELECT courtname, problem_description, contactid, international_passengers FROM ai_projects WHERE courtname > 318\"\n", "labels": {"reads": [{"table": "ai_projects", "columns": ["courtname", "problem_description", "contactid", "international_passengers"]}], "writes": [{"table": "ods.sessions", "columns": ["courtname", "problem_description", "contactid", "international_passengers"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO player_demographics SELECT * FROM legacy\ncur.execute(\"SELECT maxoccupancy, co2_reduction FROM user_ad_interactions LIMIT 73\")\n", "labels": {"reads": [{"table": "user_ad_interactions", "columns": ["maxoccupancy", "co2_reduction"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO tech_for_social_good SELECT trip_end_time, dockingid FROM dws.dws_risk_score_df WHERE trip_end_time > 433\"], check=True)\n", "labels": {"reads": [{"table": "dws.dws_risk_score_df", "columns": ["trip_end_time", "dockingid"]}], "writes": [{"table": "tech_for_social_good", "columns": ["trip_end_time", "dockingid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM food_justice_orgs\"\n", "labels": {"reads": [{"table": "food_justice_orgs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT theatrename, outcome_type FROM business_rates LIMIT 70\")\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO tunnels SELECT sighting_date, oppose_rate, date_in_location_from FROM dwd.dwd_events_delta WHERE sighting_date > 61\")\n", "labels": {"reads": [{"table": "business_rates", "columns": ["theatrename", "outcome_type"]}, {"table": "dwd.dwd_events_delta", "columns": ["sighting_date", "oppose_rate", "date_in_location_from"]}], "writes": [{"table": "tunnels", "columns": ["sighting_date", "oppose_rate", "date_in_location_from"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table enrollments --target-dir /tmp/land\n", "labels": {"reads": [{"table": "enrollments", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model seafoodsouthafricakenya depends on carbon_sequestration\ndbt build -s seafoodsouthafricakenya --vars '{\"src\":\"carbon_sequestration\"}'\n", "labels": {"reads": [{"table": "carbon_sequestration", "columns": null}], "writes": [{"table": "seafoodsouthafricakenya", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO systems (patient_count, mouse_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "systems", "columns": ["patient_count", "mouse_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO community_education_programs SELECT virtual_tour_views, programarea FROM legislation WHERE virtual_tour_views > 235\"\n", "labels": {"reads": [{"table": "legislation", "columns": ["virtual_tour_views", "programarea"]}], "writes": [{"table": "community_education_programs", "columns": ["virtual_tour_views", "programarea"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dws.dws_refunds_daily SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO water_distribution (impressions, extraction_state) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "water_distribution", "columns": ["impressions", "extraction_state"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO busmaintenance SELECT frameworkcountry, job_title_code FROM cosmetic_sales WHERE frameworkcountry > 183\")\n", "labels": {"reads": [{"table": "cosmetic_sales", "columns": ["frameworkcountry", "job_title_code"]}], "writes": [{"table": "busmaintenance", "columns": ["frameworkcountry", "job_title_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mart.campaigns_full (start_time, enrollment) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mart.campaigns_full", "columns": ["start_time", "enrollment"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT caloric_content, ironid FROM forest_species LIMIT 222\")\nimport logging\nspark.sql(\"INSERT INTO courtcases SELECT menuitemid, enzyme_id, initiative_name, machinery_id FROM inclusivehousingpolicies WHERE menuitemid > 342\")\n", "labels": {"reads": [{"table": "forest_species", "columns": ["caloric_content", "ironid"]}, {"table": "inclusivehousingpolicies", "columns": ["menuitemid", "enzyme_id", "initiative_name", "machinery_id"]}], "writes": [{"table": "courtcases", "columns": ["menuitemid", "enzyme_id", "initiative_name", "machinery_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO store_product SELECT * FROM legacy\ncur.execute(\"SELECT email, wellid FROM wind_energy_projects LIMIT 56\")\n", "labels": {"reads": [{"table": "wind_energy_projects", "columns": ["email", "wellid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO customer_master_index SELECT energy_star_rating, moisture_level FROM developers WHERE energy_star_rating > 185\"\n", "labels": {"reads": [{"table": "developers", "columns": ["energy_star_rating", "moisture_level"]}], "writes": [{"table": "customer_master_index", "columns": ["energy_star_rating", "moisture_level"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"invoice\")\nsrc.write.insertInto(\"submission\", overwrite=True)\n", "labels": {"reads": [{"table": "invoice", "columns": null}], "writes": [{"table": "submission", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"evidence_based_policies\")\nsrc.write.insertInto(\"biodiversity\", overwrite=True)\n", "labels": {"reads": [{"table": "evidence_based_policies", "columns": null}], "writes": [{"table": "biodiversity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO incarcerated SELECT grantamount, away_team FROM vessel_capacity WHERE grantamount > 278\"\n", "labels": {"reads": [{"table": "vessel_capacity", "columns": ["grantamount", "away_team"]}], "writes": [{"table": "incarcerated", "columns": ["grantamount", "away_team"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM military_personnel_africa\"\n", "labels": {"reads": [{"table": "military_personnel_africa", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO recyclers SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO claims SELECT shipping_agent_code, apt_id FROM communitypolicingcenters WHERE shipping_agent_code > 178\"\n", "labels": {"reads": [{"table": "communitypolicingcenters", "columns": ["shipping_agent_code", "apt_id"]}], "writes": [{"table": "claims", "columns": ["shipping_agent_code", "apt_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO military_spending SELECT trench_name, market_share, incidents, volunteer_id FROM waterconservation WHERE trench_name > 346\"], check=True)\n", "labels": {"reads": [{"table": "waterconservation", "columns": ["trench_name", "market_share", "incidents", "volunteer_id"]}], "writes": [{"table": "military_spending", "columns": ["trench_name", "market_share", "incidents", "volunteer_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table satellites_by_country --columns employmentdate,q1_2022_views --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "satellites_by_country", "columns": ["employmentdate", "q1_2022_views"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO weekly_weather (played, treatment) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "weekly_weather", "columns": ["played", "treatment"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table adaptation_projects --columns region_id,watertemp --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "adaptation_projects", "columns": ["region_id", "watertemp"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO communityhealthworkerscanada SELECT * FROM legacy\ncur.execute(\"SELECT creation_year, dockingdate FROM timber_production LIMIT 333\")\n", "labels": {"reads": [{"table": "timber_production", "columns": ["creation_year", "dockingdate"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO party_events SELECT * FROM legacy\ncur.execute(\"SELECT equipment_type, rehab_date FROM port_visits LIMIT 349\")\n", "labels": {"reads": [{"table": "port_visits", "columns": ["equipment_type", "rehab_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO runs SELECT kids, product_category FROM dw.dw_payments_full WHERE kids > 82\")\n", "labels": {"reads": [{"table": "dw.dw_payments_full", "columns": ["kids", "product_category"]}], "writes": [{"table": "runs", "columns": ["kids", "product_category"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.disability_type > 130).all()\n# src table: problem_log\nengine.execute(\"INSERT INTO benefits_overpayments SELECT * FROM problem_log\")\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": [{"table": "benefits_overpayments", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT chemical_id, community_members FROM impact_asia LIMIT 229;\nINSERT INTO community_policing SELECT accessibility, document_type_name FROM auctions WHERE accessibility > 36;\nSQL\n", "labels": {"reads": [{"table": "impact_asia", "columns": ["chemical_id", "community_members"]}, {"table": "auctions", "columns": ["accessibility", "document_type_name"]}], "writes": [{"table": "community_policing", "columns": ["accessibility", "document_type_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO skincareinventory SELECT 1\"\ntrap 'echo failed' ERR\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT num_volunteers, mar FROM media_types LIMIT 473\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "media_types", "columns": ["num_volunteers", "mar"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_orders_hourly\", conn)\ndf.to_sql(\"assets_frameworks\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": null}], "writes": [{"table": "assets_frameworks", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO livestock (operation, s_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "livestock", "columns": ["operation", "s_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicle_counts\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"sales_2\")\n", "labels": {"reads": [{"table": "vehicle_counts", "columns": null}], "writes": [{"table": "sales_2", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO audience SELECT engineer_id, director FROM eu_data_usage WHERE engineer_id > 366;\nEOF\n", "labels": {"reads": [{"table": "eu_data_usage", "columns": ["engineer_id", "director"]}], "writes": [{"table": "audience", "columns": ["engineer_id", "director"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM disease_prevalence\", conn)\ndf.to_sql(\"conservation_initiatives\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "disease_prevalence", "columns": null}], "writes": [{"table": "conservation_initiatives", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table evsales --target-dir /tmp/land\n", "labels": {"reads": [{"table": "evsales", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO convictions SELECT a.game_id, b.inspection_date FROM train_station a JOIN contract_states b ON a.prod_date = b.prod_date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "train_station", "columns": null}, {"table": "contract_states", "columns": null}], "writes": [{"table": "convictions", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO policyadvocacyevents SELECT stu_phone, claim_id, yield_per_acre FROM feedback WHERE stu_phone > 414\"\n", "labels": {"reads": [{"table": "feedback", "columns": ["stu_phone", "claim_id", "yield_per_acre"]}], "writes": [{"table": "policyadvocacyevents", "columns": ["stu_phone", "claim_id", "yield_per_acre"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO rural_feeder_roads SELECT precedent_id, review_rating, amount_of_refund, date_valid_to FROM industrial_customers WHERE precedent_id > 160\"\n", "labels": {"reads": [{"table": "industrial_customers", "columns": ["precedent_id", "review_rating", "amount_of_refund", "date_valid_to"]}], "writes": [{"table": "rural_feeder_roads", "columns": ["precedent_id", "review_rating", "amount_of_refund", "date_valid_to"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO assignedto SELECT total_employees, password, destroyed_by_employee_id FROM attendance WHERE total_employees > 312;\nEOF\n", "labels": {"reads": [{"table": "attendance", "columns": ["total_employees", "password", "destroyed_by_employee_id"]}], "writes": [{"table": "assignedto", "columns": ["total_employees", "password", "destroyed_by_employee_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO vrplayers SELECT a.breed, b.productcategory FROM publication a JOIN autonomous_research b ON a.fundingagency = b.fundingagency\"\n", "labels": {"reads": [{"table": "publication", "columns": null}, {"table": "autonomous_research", "columns": null}], "writes": [{"table": "vrplayers", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO storage SELECT research_name, gamesplayed, is_operational, testdate FROM co2_sequestration WHERE research_name > 157\"], check=True)\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": ["research_name", "gamesplayed", "is_operational", "testdate"]}], "writes": [{"table": "storage", "columns": ["research_name", "gamesplayed", "is_operational", "testdate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO threat_intelligence_budget SELECT a.quantity_sold, b.budgetid FROM space_exploration a JOIN gamereviews b ON a.iata = b.iata\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "space_exploration", "columns": null}, {"table": "gamereviews", "columns": null}], "writes": [{"table": "threat_intelligence_budget", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO customer_month SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"circular_economy\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean\")\n", "labels": {"reads": [{"table": "circular_economy", "columns": null}], "writes": [{"table": "ocean", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO factories_africa (pd_id, appointmentid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "factories_africa", "columns": ["pd_id", "appointmentid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.service_name > 22).all()\n# src table: mexico_regions\nengine.execute(\"INSERT INTO auto_shows SELECT * FROM mexico_regions\")\n", "labels": {"reads": [{"table": "mexico_regions", "columns": null}], "writes": [{"table": "auto_shows", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.dwd_events_delta (city_traffic_speed, album_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.dwd_events_delta", "columns": ["city_traffic_speed", "album_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.campaigns\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"demographics\")\n", "labels": {"reads": [{"table": "dwd.campaigns", "columns": null}], "writes": [{"table": "demographics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"atlantic_marine_life\").toPandas()\ndf[[\"equipment_type\", \"budget\"]].to_sql(\"smart_city_projects\", engine, index=False)\n", "labels": {"reads": [{"table": "atlantic_marine_life", "columns": null}], "writes": [{"table": "smart_city_projects", "columns": ["equipment_type", "budget"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO teacher_pd (facility_id, tier) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "teacher_pd", "columns": ["facility_id", "tier"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table emergencyservices --columns implementation_year,animal --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "emergencyservices", "columns": ["implementation_year", "animal"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dependent\").toPandas()\ndf[[\"createdate\", \"amount_payment\"]].to_sql(\"agricultural_innovation\", engine, index=False)\n", "labels": {"reads": [{"table": "dependent", "columns": null}], "writes": [{"table": "agricultural_innovation", "columns": ["createdate", "amount_payment"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO temperaturerecords SELECT official_native_language, investmenttype, medical_professional_id FROM audience WHERE official_native_language > 290;\nEOF\n", "labels": {"reads": [{"table": "audience", "columns": ["official_native_language", "investmenttype", "medical_professional_id"]}], "writes": [{"table": "temperaturerecords", "columns": ["official_native_language", "investmenttype", "medical_professional_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT trial_status, policy_type FROM open_pedagogy_courses\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"autonomous_research\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "open_pedagogy_courses", "columns": ["trial_status", "policy_type"]}], "writes": [{"table": "autonomous_research", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO menu_engineering SELECT complete_date, courses FROM fairtradecertification WHERE complete_date > 52\"\n", "labels": {"reads": [{"table": "fairtradecertification", "columns": ["complete_date", "courses"]}], "writes": [{"table": "menu_engineering", "columns": ["complete_date", "courses"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO purchase SELECT a.founder_veteran, b.diversity_score FROM browser a JOIN mining.company b ON a.participant_details = b.participant_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "browser", "columns": null}, {"table": "mining.company", "columns": null}], "writes": [{"table": "purchase", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO urban_transportation SELECT courtname, name_last, sculpture_name, area_type FROM animal_population_status WHERE courtname > 288\"], check=True)\n", "labels": {"reads": [{"table": "animal_population_status", "columns": ["courtname", "name_last", "sculpture_name", "area_type"]}], "writes": [{"table": "urban_transportation", "columns": ["courtname", "name_last", "sculpture_name", "area_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO initiatives SELECT grant_date, carrierid, num_shariah_compliant_investments, dec FROM storage_projects WHERE grant_date > 340\"\n", "labels": {"reads": [{"table": "storage_projects", "columns": ["grant_date", "carrierid", "num_shariah_compliant_investments", "dec"]}], "writes": [{"table": "initiatives", "columns": ["grant_date", "carrierid", "num_shariah_compliant_investments", "dec"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bus_fare_collection SELECT a.cost_id, b.waste_id FROM marinespeciesobservations a JOIN marketing_budgets b ON a.max_dew_point_f = b.max_dew_point_f\"\n", "labels": {"reads": [{"table": "marinespeciesobservations", "columns": null}, {"table": "marketing_budgets", "columns": null}], "writes": [{"table": "bus_fare_collection", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"strains\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"textile_suppliers\")\n", "labels": {"reads": [{"table": "strains", "columns": null}], "writes": [{"table": "textile_suppliers", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO round SELECT founders_lgbtq, goldquantity FROM heritage_sites WHERE founders_lgbtq > 407\"\n", "labels": {"reads": [{"table": "heritage_sites", "columns": ["founders_lgbtq", "goldquantity"]}], "writes": [{"table": "round", "columns": ["founders_lgbtq", "goldquantity"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"flight_emissions\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"resilience_infrastructure\")\n", "labels": {"reads": [{"table": "flight_emissions", "columns": null}], "writes": [{"table": "resilience_infrastructure", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mart.member_point_df --columns country_of_origin,organized_by --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mart.member_point_df", "columns": ["country_of_origin", "organized_by"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT wellbeing_score, claim_amount FROM bi.bi_exposure_hourly LIMIT 420\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "bi.bi_exposure_hourly", "columns": ["wellbeing_score", "claim_amount"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model ticketspending depends on complaints\ndbt run -s ticketspending --vars '{\"source_table\":\"complaints\"}'\n", "labels": {"reads": [{"table": "complaints", "columns": null}], "writes": [{"table": "ticketspending", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO materials_usage SELECT receipt_date, party FROM agro_regions WHERE receipt_date > 154;\nEOF\n", "labels": {"reads": [{"table": "agro_regions", "columns": ["receipt_date", "party"]}], "writes": [{"table": "materials_usage", "columns": ["receipt_date", "party"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO dws.dws_events_df SELECT last_workout_date, airline, other_item_details FROM model_fairness WHERE last_workout_date > 235\")\n", "labels": {"reads": [{"table": "model_fairness", "columns": ["last_workout_date", "airline", "other_item_details"]}], "writes": [{"table": "dws.dws_events_df", "columns": ["last_workout_date", "airline", "other_item_details"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rating\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"camera_lens\")\n", "labels": {"reads": [{"table": "rating", "columns": null}], "writes": [{"table": "camera_lens", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO browser SELECT a.technique, b.startup_id FROM news_stories a JOIN students_lifelong_learning b ON a.capital = b.capital\"\n", "labels": {"reads": [{"table": "news_stories", "columns": null}, {"table": "students_lifelong_learning", "columns": null}], "writes": [{"table": "browser", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT dose, oil_production FROM electricvehiclestats LIMIT 73\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "electricvehiclestats", "columns": ["dose", "oil_production"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"asset_parts\")\nsrc.write.insertInto(\"coowners\", overwrite=True)\n", "labels": {"reads": [{"table": "asset_parts", "columns": null}], "writes": [{"table": "coowners", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO customer_size_diversity SELECT eia_date, restaurantid, menucategory, party_phone FROM electricvehiclestats WHERE eia_date > 2;\nEOF\n", "labels": {"reads": [{"table": "electricvehiclestats", "columns": ["eia_date", "restaurantid", "menucategory", "party_phone"]}], "writes": [{"table": "customer_size_diversity", "columns": ["eia_date", "restaurantid", "menucategory", "party_phone"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainable_urban_properties_2 SELECT museum_details, involved_in_lifelong_learning FROM biosensors.projects WHERE museum_details > 498\"\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": ["museum_details", "involved_in_lifelong_learning"]}], "writes": [{"table": "sustainable_urban_properties_2", "columns": ["museum_details", "involved_in_lifelong_learning"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ods.ods_events_daily (artifact_weight, max_speed) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ods.ods_events_daily", "columns": ["artifact_weight", "max_speed"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.bi_sessions_hourly SELECT 1\"\nmkdir -p /tmp/joblog\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gamegenres\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gamegenres", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_source(ctx, \"trains\")\npersist_to_output(df, \"community_centers\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "trains", "columns": null}], "writes": [{"table": "community_centers", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT monthly_rental, participant FROM port\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"dwd.dwd_campaigns_df\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "port", "columns": ["monthly_rental", "participant"]}], "writes": [{"table": "dwd.dwd_campaigns_df", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT marketing_region_name, dispensary_name FROM ai_for_social_good LIMIT 36\")\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nimport logging\nspark.sql(\"INSERT INTO iron_ore_production SELECT playdate, birthdate FROM broadband_customers_global WHERE playdate > 312\")\n", "labels": {"reads": [{"table": "ai_for_social_good", "columns": ["marketing_region_name", "dispensary_name"]}, {"table": "broadband_customers_global", "columns": ["playdate", "birthdate"]}], "writes": [{"table": "iron_ore_production", "columns": ["playdate", "birthdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"retailerg\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"attorney_billing\")\n", "labels": {"reads": [{"table": "retailerg", "columns": null}], "writes": [{"table": "attorney_billing", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.num_students > 366).all()\n# src table: workshop\nengine.execute(\"INSERT INTO bi.refunds_daily SELECT * FROM workshop\")\n", "labels": {"reads": [{"table": "workshop", "columns": null}], "writes": [{"table": "bi.refunds_daily", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"police_stations\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"festivals\")\n", "labels": {"reads": [{"table": "police_stations", "columns": null}], "writes": [{"table": "festivals", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO courtcases SELECT a.forest_type, b.element FROM vesselarrivals a JOIN postseason b ON a.assistingnurse = b.assistingnurse\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "vesselarrivals", "columns": null}, {"table": "postseason", "columns": null}], "writes": [{"table": "courtcases", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vehicles\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "vehicles", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table acceptance --target-dir /tmp/land\n", "labels": {"reads": [{"table": "acceptance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ethical_certifications > 40).all()\n# src table: worker_scores\nengine.execute(\"INSERT INTO host SELECT * FROM worker_scores\")\n", "labels": {"reads": [{"table": "worker_scores", "columns": null}], "writes": [{"table": "host", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = read_dataset(ctx, \"regional_railways\")\npersist_to_target(df, \"food_items\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "regional_railways", "columns": null}], "writes": [{"table": "food_items", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM tryout\"\n", "labels": {"reads": [{"table": "tryout", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table products_booked --columns image_data,special_features --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "products_booked", "columns": ["image_data", "special_features"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ads.ads_exposure_di\")\nsrc.write.insertInto(\"research.species\", overwrite=True)\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": null}], "writes": [{"table": "research.species", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO match_result SELECT a.postal_code, b.trip_type FROM ads.ads_exposure_daily a JOIN containers b ON a.characteristic_name = b.characteristic_name\"\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": null}, {"table": "containers", "columns": null}], "writes": [{"table": "match_result", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO military_expenditure (heritage_site_id, document_code) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "military_expenditure", "columns": ["heritage_site_id", "document_code"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ods.ods_risk_score_full SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"farmers_india\")\nsrc.write.insertInto(\"state_water_usage\", overwrite=True)\n", "labels": {"reads": [{"table": "farmers_india", "columns": null}], "writes": [{"table": "state_water_usage", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO repair_assignment (meter_300, building_address) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "repair_assignment", "columns": ["meter_300", "building_address"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO attendee_demographics SELECT a.farmland_id, b.product_price FROM medical_professionals a JOIN indie_artists b ON a.inclusive = b.inclusive\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "medical_professionals", "columns": null}, {"table": "indie_artists", "columns": null}], "writes": [{"table": "attendee_demographics", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT postal_code, concertid FROM technology_access\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\ndf.to_sql(\"investments\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "technology_access", "columns": ["postal_code", "concertid"]}], "writes": [{"table": "investments", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO strategies SELECT a.rooms, b.contractid FROM agencies a JOIN apartment_buildings b ON a.trip_id = b.trip_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "agencies", "columns": null}, {"table": "apartment_buildings", "columns": null}], "writes": [{"table": "strategies", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO ingredient SELECT join_date, appointment_duration FROM functional_areas WHERE join_date > 340;\nEOF\n", "labels": {"reads": [{"table": "functional_areas", "columns": ["join_date", "appointment_duration"]}], "writes": [{"table": "ingredient", "columns": ["join_date", "appointment_duration"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO complaints SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO stg.stg_campaigns_hourly SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT driverid, zipcode FROM fleets LIMIT 51\")\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO carbon_offset_south_america SELECT resource_id, sessiondate FROM employment WHERE resource_id > 480\")\n", "labels": {"reads": [{"table": "fleets", "columns": ["driverid", "zipcode"]}, {"table": "employment", "columns": ["resource_id", "sessiondate"]}], "writes": [{"table": "carbon_offset_south_america", "columns": ["resource_id", "sessiondate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ethicalaibudget\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "ethicalaibudget", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO ai_ethics_policies SELECT catalog_publisher, bdate, cuisine_name FROM culturalcompetency WHERE catalog_publisher > 105\"\n", "labels": {"reads": [{"table": "culturalcompetency", "columns": ["catalog_publisher", "bdate", "cuisine_name"]}], "writes": [{"table": "ai_ethics_policies", "columns": ["catalog_publisher", "bdate", "cuisine_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT menuitem, policyid FROM biosensors.readings\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"low_value_contracts\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "biosensors.readings", "columns": ["menuitem", "policyid"]}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"underwater_cables\")\nsrc.write.insertInto(\"fair_trade_brands\", overwrite=True)\n", "labels": {"reads": [{"table": "underwater_cables", "columns": null}], "writes": [{"table": "fair_trade_brands", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_adaptation\"\n", "labels": {"reads": [{"table": "climate_adaptation", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table contract_transactions --columns level,rec_engine --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "contract_transactions", "columns": ["level", "rec_engine"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.prominence > 466).all()\n# src table: policyholders\nengine.execute(\"INSERT INTO broadband_subscribers SELECT * FROM policyholders\")\n", "labels": {"reads": [{"table": "policyholders", "columns": null}], "writes": [{"table": "broadband_subscribers", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT label_id, catalog_level_number FROM roles LIMIT 399;\nINSERT INTO storage_projects SELECT follow_up_date, start_station_name FROM animal_populations WHERE follow_up_date > 296;\nSQL\n", "labels": {"reads": [{"table": "roles", "columns": ["label_id", "catalog_level_number"]}, {"table": "animal_populations", "columns": ["follow_up_date", "start_station_name"]}], "writes": [{"table": "storage_projects", "columns": ["follow_up_date", "start_station_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"galleries\").toPandas()\ndf[[\"area_id\", \"longitude\"]].to_sql(\"user_profiles\", engine, index=False)\n", "labels": {"reads": [{"table": "galleries", "columns": null}], "writes": [{"table": "user_profiles", "columns": ["area_id", "longitude"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO accessible_tech_categories SELECT noise_level, feature_details, train_id, supplierid FROM crops_table WHERE noise_level > 345\"], check=True)\n", "labels": {"reads": [{"table": "crops_table", "columns": ["noise_level", "feature_details", "train_id", "supplierid"]}], "writes": [{"table": "accessible_tech_categories", "columns": ["noise_level", "feature_details", "train_id", "supplierid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO vulnerabilities SELECT dissolved_oxygen, workforce_development, attack_id FROM wholesale_orders WHERE dissolved_oxygen > 281\"\n", "labels": {"reads": [{"table": "wholesale_orders", "columns": ["dissolved_oxygen", "workforce_development", "attack_id"]}], "writes": [{"table": "vulnerabilities", "columns": ["dissolved_oxygen", "workforce_development", "attack_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT contractid, volunteerage FROM smartcityprojects LIMIT 252\")\nmetrics.append(round(score, 4))\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO song SELECT rid, left_office, user_category FROM ods.ods_campaigns_df WHERE rid > 116\")\n", "labels": {"reads": [{"table": "smartcityprojects", "columns": ["contractid", "volunteerage"]}, {"table": "ods.ods_campaigns_df", "columns": ["rid", "left_office", "user_category"]}], "writes": [{"table": "song", "columns": ["rid", "left_office", "user_category"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO genetics.projects SELECT a.incident_count, b.fabrictype FROM mart_refunds_delta a JOIN online_platform b ON a.volunteername = b.volunteername\"\n", "labels": {"reads": [{"table": "mart_refunds_delta", "columns": null}, {"table": "online_platform", "columns": null}], "writes": [{"table": "genetics.projects", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO communityhealthworkers SELECT alert_id, artifactid, offer_id, exhibition_id FROM mart_exposure_di WHERE alert_id > 209\"\n", "labels": {"reads": [{"table": "mart_exposure_di", "columns": ["alert_id", "artifactid", "offer_id", "exhibition_id"]}], "writes": [{"table": "communityhealthworkers", "columns": ["alert_id", "artifactid", "offer_id", "exhibition_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table arctic_research --target-dir /tmp/land\n", "labels": {"reads": [{"table": "arctic_research", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO gradeconversion SELECT pilot_id, total_points FROM residents_services WHERE pilot_id > 227\"\n", "labels": {"reads": [{"table": "residents_services", "columns": ["pilot_id", "total_points"]}], "writes": [{"table": "gradeconversion", "columns": ["pilot_id", "total_points"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"chemical_processes\").toPandas()\ndf[[\"emp_num\", \"amount_piad\"]].to_sql(\"eco_materials\", engine, index=False)\n", "labels": {"reads": [{"table": "chemical_processes", "columns": null}], "writes": [{"table": "eco_materials", "columns": ["emp_num", "amount_piad"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table maintenance_schedule --target-dir /tmp/land\n", "labels": {"reads": [{"table": "maintenance_schedule", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table class --columns algorithm_name,person_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "class", "columns": ["algorithm_name", "person_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT online_dispute_resolution, played FROM musical\", engine)\nimport logging\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"life_expectancy\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "musical", "columns": ["online_dispute_resolution", "played"]}], "writes": [{"table": "life_expectancy", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO maintenance_engineers SELECT strategy_name, artworkyear FROM jupiter_spacecraft WHERE strategy_name > 410\"\n", "labels": {"reads": [{"table": "jupiter_spacecraft", "columns": ["strategy_name", "artworkyear"]}], "writes": [{"table": "maintenance_engineers", "columns": ["strategy_name", "artworkyear"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO platformh SELECT * FROM legacy\ncur.execute(\"SELECT fault_short_name, adoption_date FROM concert_sales LIMIT 322\")\n", "labels": {"reads": [{"table": "concert_sales", "columns": ["fault_short_name", "adoption_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO dali SELECT yield_id, district_id, date_of_latest_revision, volunteerjoindate FROM watertreatmentplants WHERE yield_id > 323\")\n", "labels": {"reads": [{"table": "watertreatmentplants", "columns": ["yield_id", "district_id", "date_of_latest_revision", "volunteerjoindate"]}], "writes": [{"table": "dali", "columns": ["yield_id", "district_id", "date_of_latest_revision", "volunteerjoindate"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT investor_name, violation_count FROM dws.dws_cart_item_daily LIMIT 254\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO pipelines SELECT regionid, playlist_id, operation_type FROM stg.stg_risk_score_df WHERE regionid > 326\")\n", "labels": {"reads": [{"table": "dws.dws_cart_item_daily", "columns": ["investor_name", "violation_count"]}, {"table": "stg.stg_risk_score_df", "columns": ["regionid", "playlist_id", "operation_type"]}], "writes": [{"table": "pipelines", "columns": ["regionid", "playlist_id", "operation_type"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO dwd.dwd_exposure_full SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sites\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"carbonoffsetinitiatives\")\n", "labels": {"reads": [{"table": "sites", "columns": null}], "writes": [{"table": "carbonoffsetinitiatives", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.coupon_id > 318).all()\n# src table: mental_health_parity_violations\nengine.execute(\"INSERT INTO rural_areas SELECT * FROM mental_health_parity_violations\")\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": null}], "writes": [{"table": "rural_areas", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO exit SELECT author_or_editor, exhibitionid, season FROM mental_health_parity_violations WHERE author_or_editor > 458\"], check=True)\n", "labels": {"reads": [{"table": "mental_health_parity_violations", "columns": ["author_or_editor", "exhibitionid", "season"]}], "writes": [{"table": "exit", "columns": ["author_or_editor", "exhibitionid", "season"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = source_table(ctx, \"airportdata\")\nexport_to_store(df, \"stg.stg_exposure_di\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "airportdata", "columns": null}], "writes": [{"table": "stg.stg_exposure_di", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT reason, state_code FROM digital_trends LIMIT 227\")\nrows = cur.fetchall()\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "digital_trends", "columns": ["reason", "state_code"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO deep_sea_species SELECT a.time_id, b.semester FROM ads.ads_exposure_daily a JOIN safety_testing b ON a.copy_number = b.copy_number\"\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": null}, {"table": "safety_testing", "columns": null}], "writes": [{"table": "deep_sea_species", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO fabricdata SELECT * FROM legacy\ncur.execute(\"SELECT policy_type, co_owner_count FROM road_construction LIMIT 492\")\n", "labels": {"reads": [{"table": "road_construction", "columns": ["policy_type", "co_owner_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO peakhours SELECT total_points, offer_id, awardid, plan_type FROM threat_intelligence WHERE total_points > 483\"], check=True)\n", "labels": {"reads": [{"table": "threat_intelligence", "columns": ["total_points", "offer_id", "awardid", "plan_type"]}], "writes": [{"table": "peakhours", "columns": ["total_points", "offer_id", "awardid", "plan_type"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vehiclemodels SELECT product_color, node_id, num_investments FROM financial_capability_id WHERE product_color > 162\"\n", "labels": {"reads": [{"table": "financial_capability_id", "columns": ["product_color", "node_id", "num_investments"]}], "writes": [{"table": "vehiclemodels", "columns": ["product_color", "node_id", "num_investments"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"teacher_development_race\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gameattendance\").toPandas()\ndf[[\"cname\", \"team_id_loser\"]].to_sql(\"sustainablebrands\", engine, index=False)\n", "labels": {"reads": [{"table": "gameattendance", "columns": null}], "writes": [{"table": "sustainablebrands", "columns": ["cname", "team_id_loser"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dp_articles\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"performance_scores\")\n", "labels": {"reads": [{"table": "dp_articles", "columns": null}], "writes": [{"table": "performance_scores", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO wins SELECT part_name, countryid, task_details, members FROM harvest_permits WHERE part_name > 98;\nEOF\n", "labels": {"reads": [{"table": "harvest_permits", "columns": ["part_name", "countryid", "task_details", "members"]}], "writes": [{"table": "wins", "columns": ["part_name", "countryid", "task_details", "members"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dws_shipments_df\", conn)\ndf.to_sql(\"soccer_goals\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dws_shipments_df", "columns": null}], "writes": [{"table": "soccer_goals", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nsql = \"INSERT INTO ads.users_full SELECT a.songname, b.days FROM therapy a JOIN social_impact_bonds b ON a.feature_id = b.feature_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "therapy", "columns": null}, {"table": "social_impact_bonds", "columns": null}], "writes": [{"table": "ads.users_full", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM climate_finance_asia\", conn)\ndf.to_sql(\"branch\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": null}], "writes": [{"table": "branch", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT invested, founder FROM bi_campaigns_delta\", engine)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\ndf.to_sql(\"follows\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "bi_campaigns_delta", "columns": ["invested", "founder"]}], "writes": [{"table": "follows", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT mental_health_score, treasurer_vote FROM dwd.coupon_use_full\", engine)\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"water_distribution\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dwd.coupon_use_full", "columns": ["mental_health_score", "treasurer_vote"]}], "writes": [{"table": "water_distribution", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_payments_df\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.mart_payments_df", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO albums SELECT a.round_amount, b.booking_id FROM dws_products a JOIN drug_approvals b ON a.operationname = b.operationname\"\n", "labels": {"reads": [{"table": "dws_products", "columns": null}, {"table": "drug_approvals", "columns": null}], "writes": [{"table": "albums", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table communityengagementmetrics --target-dir /tmp/land\n", "labels": {"reads": [{"table": "communityengagementmetrics", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT union_member, product_quantity FROM economic_diversification_projects\", engine)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"sportsinfo\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "economic_diversification_projects", "columns": ["union_member", "product_quantity"]}], "writes": [{"table": "sportsinfo", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"volume\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "volume", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT nutrient_level, comment_count FROM minor_in LIMIT 270;\nINSERT INTO indian_ocean_fishingvessels SELECT institution_name, workshop_name, neighborhood_id, community_members FROM spacecraftspeed WHERE institution_name > 437;\nSQL\n", "labels": {"reads": [{"table": "minor_in", "columns": ["nutrient_level", "comment_count"]}, {"table": "spacecraftspeed", "columns": ["institution_name", "workshop_name", "neighborhood_id", "community_members"]}], "writes": [{"table": "indian_ocean_fishingvessels", "columns": ["institution_name", "workshop_name", "neighborhood_id", "community_members"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO caseattorneys SELECT * FROM legacy\ncur.execute(\"SELECT animal, station_name FROM patienttreatments LIMIT 160\")\n", "labels": {"reads": [{"table": "patienttreatments", "columns": ["animal", "station_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO factories (has_disability, primary_conference) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "factories", "columns": ["has_disability", "primary_conference"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO gender SELECT max_depth, recipe_id, drill_count, trainingtype FROM garments WHERE max_depth > 449\"\n", "labels": {"reads": [{"table": "garments", "columns": ["max_depth", "recipe_id", "drill_count", "trainingtype"]}], "writes": [{"table": "gender", "columns": ["max_depth", "recipe_id", "drill_count", "trainingtype"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO african_tourism SELECT * FROM legacy\ncur.execute(\"SELECT asset_model, editor_id FROM animals LIMIT 276\")\n", "labels": {"reads": [{"table": "animals", "columns": ["asset_model", "editor_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO audience_demographics SELECT artwork_name, discovered_date, median_home_value, cuisine_id FROM performingartsprograms WHERE artwork_name > 472\"\n", "labels": {"reads": [{"table": "performingartsprograms", "columns": ["artwork_name", "discovered_date", "median_home_value", "cuisine_id"]}], "writes": [{"table": "audience_demographics", "columns": ["artwork_name", "discovered_date", "median_home_value", "cuisine_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nsql = \"INSERT INTO prescribes SELECT a.language, b.inventor_name FROM vessel_safety a JOIN e_scooter_trips b ON a.hometeam = b.hometeam\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "vessel_safety", "columns": null}, {"table": "e_scooter_trips", "columns": null}], "writes": [{"table": "prescribes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM dwd_sessions_hourly\", conn)\ndf.to_sql(\"authors\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "dwd_sessions_hourly", "columns": null}], "writes": [{"table": "authors", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO athletes SELECT a.dateundergoes, b.artistname FROM dwd.dwd_events_delta a JOIN cuisine b ON a.trial_name = b.trial_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dwd.dwd_events_delta", "columns": null}, {"table": "cuisine", "columns": null}], "writes": [{"table": "athletes", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO electric_buses SELECT section_title, card_id, workshop_group_id, wildlife_type_id FROM department WHERE section_title > 485\"\n", "labels": {"reads": [{"table": "department", "columns": ["section_title", "card_id", "workshop_group_id", "wildlife_type_id"]}], "writes": [{"table": "electric_buses", "columns": ["section_title", "card_id", "workshop_group_id", "wildlife_type_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT measurement_id, unsure_rate FROM retailerg LIMIT 490\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO mentalhealthparityviolations SELECT founder_group, document_code, ingredient FROM satelliteimagery WHERE founder_group > 196\")\n", "labels": {"reads": [{"table": "retailerg", "columns": ["measurement_id", "unsure_rate"]}, {"table": "satelliteimagery", "columns": ["founder_group", "document_code", "ingredient"]}], "writes": [{"table": "mentalhealthparityviolations", "columns": ["founder_group", "document_code", "ingredient"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nspark.sql(\"INSERT INTO dws_events_di SELECT comment_count, opponent_id, min_temperature_f, height FROM country_renewable_energy WHERE comment_count > 320\")\n", "labels": {"reads": [{"table": "country_renewable_energy", "columns": ["comment_count", "opponent_id", "min_temperature_f", "height"]}], "writes": [{"table": "dws_events_di", "columns": ["comment_count", "opponent_id", "min_temperature_f", "height"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT therapy_id, publication_year FROM entrepreneur\", engine)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ndf.to_sql(\"articles_es\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "entrepreneur", "columns": ["therapy_id", "publication_year"]}], "writes": [{"table": "articles_es", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO person (recruiterid, firstdonationdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "person", "columns": ["recruiterid", "firstdonationdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table hotel_tech_adoption --target-dir /tmp/land\n", "labels": {"reads": [{"table": "hotel_tech_adoption", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO maintenance_engineers (production_id, gas_production_2020) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "maintenance_engineers", "columns": ["production_id", "gas_production_2020"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO counties (center, firstname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "counties", "columns": ["center", "firstname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"vrgames\")\nsrc.write.insertInto(\"manufacturersustainability\", overwrite=True)\n", "labels": {"reads": [{"table": "vrgames", "columns": null}], "writes": [{"table": "manufacturersustainability", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"accelerator_compatible_browser\")\nsrc.write.insertInto(\"class\", overwrite=True)\n", "labels": {"reads": [{"table": "accelerator_compatible_browser", "columns": null}], "writes": [{"table": "class", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_source(ctx, \"hotels\")\nsave_to_target(df, \"rainfall_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "hotels", "columns": null}], "writes": [{"table": "rainfall_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table office_locations --columns college,user_login --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "office_locations", "columns": ["college", "user_login"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM wastewater_plants\", conn)\ndf.to_sql(\"musical\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": null}], "writes": [{"table": "musical", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO milestones (customer_id, vehicleid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "milestones", "columns": ["customer_id", "vehicleid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.license_plate > 16).all()\n# src table: vrplayers\nengine.execute(\"INSERT INTO medicine SELECT * FROM vrplayers\")\n", "labels": {"reads": [{"table": "vrplayers", "columns": null}], "writes": [{"table": "medicine", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"unions\").toPandas()\ndf[[\"area_sqkm\", \"producerid\"]].to_sql(\"studies\", engine, index=False)\n", "labels": {"reads": [{"table": "unions", "columns": null}], "writes": [{"table": "studies", "columns": ["area_sqkm", "producerid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dws.dws_inventory_hourly\"\n", "labels": {"reads": [{"table": "dws.dws_inventory_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "product", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO wind_turbines SELECT assessmentdate, date_claim_made, date_of_completion FROM conditions WHERE assessmentdate > 317\")\n", "labels": {"reads": [{"table": "conditions", "columns": ["assessmentdate", "date_claim_made", "date_of_completion"]}], "writes": [{"table": "wind_turbines", "columns": ["assessmentdate", "date_claim_made", "date_of_completion"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model mart_campaigns_daily depends on energy_prices\ndbt build --select mart_campaigns_daily --vars '{\"source_table\":\"energy_prices\"}'\n", "labels": {"reads": [{"table": "energy_prices", "columns": null}], "writes": [{"table": "mart_campaigns_daily", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO australia_offset_programs SELECT * FROM legacy\ncur.execute(\"SELECT hourid, staff_first_name FROM mart.clicks_delta LIMIT 203\")\n", "labels": {"reads": [{"table": "mart.clicks_delta", "columns": ["hourid", "staff_first_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"council_tax\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"pilot\")\n", "labels": {"reads": [{"table": "council_tax", "columns": null}], "writes": [{"table": "pilot", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO irrigation_systems SELECT born_state, authors, count_date, system_name FROM green_certification WHERE born_state > 101\")\n", "labels": {"reads": [{"table": "green_certification", "columns": ["born_state", "authors", "count_date", "system_name"]}], "writes": [{"table": "irrigation_systems", "columns": ["born_state", "authors", "count_date", "system_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM publicchargingstations\"\n", "labels": {"reads": [{"table": "publicchargingstations", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_campaigns_delta (testtypeid, eventid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_campaigns_delta", "columns": ["testtypeid", "eventid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ocean_salinity (loan_id, production_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ocean_salinity", "columns": ["loan_id", "production_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"rnd_budget\")\nsrc.write.insertInto(\"coach\", overwrite=True)\n", "labels": {"reads": [{"table": "rnd_budget", "columns": null}], "writes": [{"table": "coach", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"skincareproducts\").toPandas()\ndf[[\"population\", \"resource_id\"]].to_sql(\"recycling_rates_state\", engine, index=False)\n", "labels": {"reads": [{"table": "skincareproducts", "columns": null}], "writes": [{"table": "recycling_rates_state", "columns": ["population", "resource_id"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO state_usage SELECT student_id, purchaseid FROM mart.mart_coupon_use_full WHERE student_id > 486\"\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_full", "columns": ["student_id", "purchaseid"]}], "writes": [{"table": "state_usage", "columns": ["student_id", "purchaseid"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO platform SELECT invoice_id, mid, environmental_impact_score FROM patient_outcomes WHERE invoice_id > 379\"\n", "labels": {"reads": [{"table": "patient_outcomes", "columns": ["invoice_id", "mid", "environmental_impact_score"]}], "writes": [{"table": "platform", "columns": ["invoice_id", "mid", "environmental_impact_score"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO heritage_sites_3 SELECT school_colors, line_number, recruitername FROM scan_dates WHERE school_colors > 109\"\n", "labels": {"reads": [{"table": "scan_dates", "columns": ["school_colors", "line_number", "recruitername"]}], "writes": [{"table": "heritage_sites_3", "columns": ["school_colors", "line_number", "recruitername"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT apid, amount_due FROM workforce_development LIMIT 159\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "workforce_development", "columns": ["apid", "amount_due"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT restypedescription, sustainable_practice FROM fuel_consumption LIMIT 313;\nINSERT INTO organic_products SELECT avg_depth, purchase_transaction_id FROM pilot_record WHERE avg_depth > 344;\nSQL\n", "labels": {"reads": [{"table": "fuel_consumption", "columns": ["restypedescription", "sustainable_practice"]}, {"table": "pilot_record", "columns": ["avg_depth", "purchase_transaction_id"]}], "writes": [{"table": "organic_products", "columns": ["avg_depth", "purchase_transaction_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dw.inventory_delta (disaster_id, shipmenttype) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dw.inventory_delta", "columns": ["disaster_id", "shipmenttype"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT accreditation_type, org FROM product_sales\", engine)\nimport logging\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"atlantic_marine_life\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "product_sales", "columns": ["accreditation_type", "org"]}], "writes": [{"table": "atlantic_marine_life", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM battery_projects\", conn)\ndf.to_sql(\"low_value_contracts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "battery_projects", "columns": null}], "writes": [{"table": "low_value_contracts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT number_cities, production_mwh FROM steps LIMIT 366\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO diplomacy_events SELECT app_name, home_city, museum_id, bioprocess_id FROM strains WHERE app_name > 306\")\n", "labels": {"reads": [{"table": "steps", "columns": ["number_cities", "production_mwh"]}, {"table": "strains", "columns": ["app_name", "home_city", "museum_id", "bioprocess_id"]}], "writes": [{"table": "diplomacy_events", "columns": ["app_name", "home_city", "museum_id", "bioprocess_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO shipments SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO waste_types (customer_first_name, community_type) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "waste_types", "columns": ["customer_first_name", "community_type"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO vrgames SELECT date_of_completion, organization_name, artist, funding_source FROM bi.bi_payments_delta WHERE date_of_completion > 301\"\n", "labels": {"reads": [{"table": "bi.bi_payments_delta", "columns": ["date_of_completion", "organization_name", "artist", "funding_source"]}], "writes": [{"table": "vrgames", "columns": ["date_of_completion", "organization_name", "artist", "funding_source"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO culturalcompetencytrainings SELECT dose, sustainability_id, acc_percent FROM workouts WHERE dose > 459\"\n", "labels": {"reads": [{"table": "workouts", "columns": ["dose", "sustainability_id", "acc_percent"]}], "writes": [{"table": "culturalcompetencytrainings", "columns": ["dose", "sustainability_id", "acc_percent"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rental\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"league_x\")\n", "labels": {"reads": [{"table": "rental", "columns": null}], "writes": [{"table": "league_x", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table passenger_trips --columns appointment_date,unit_name --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "passenger_trips", "columns": ["appointment_date", "unit_name"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"news_stories\")\nsrc.write.insertInto(\"carbon_sequestration\", overwrite=True)\n", "labels": {"reads": [{"table": "news_stories", "columns": null}], "writes": [{"table": "carbon_sequestration", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model fireincidents depends on producers\ndbt build -s fireincidents --vars '{\"source_table\":\"producers\"}'\n", "labels": {"reads": [{"table": "producers", "columns": null}], "writes": [{"table": "fireincidents", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.yield > 96).all()\n# src table: spacecraft_temperatures\nengine.execute(\"INSERT INTO exit_strategy SELECT * FROM spacecraft_temperatures\")\n", "labels": {"reads": [{"table": "spacecraft_temperatures", "columns": null}], "writes": [{"table": "exit_strategy", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO fair_trade_brands SELECT start_speed, university FROM art_pieces WHERE start_speed > 370\"], check=True)\n", "labels": {"reads": [{"table": "art_pieces", "columns": ["start_speed", "university"]}], "writes": [{"table": "fair_trade_brands", "columns": ["start_speed", "university"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spacecraftspeed SELECT a.purchases, b.account_id FROM english_premier_league a JOIN documents_to_be_destroyed b ON a.event_id = b.event_id\"\n", "labels": {"reads": [{"table": "english_premier_league", "columns": null}, {"table": "documents_to_be_destroyed", "columns": null}], "writes": [{"table": "spacecraftspeed", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO claims SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.ads_clicks_delta SELECT investor, num_volunteers FROM location WHERE investor > 440\"], check=True)\n", "labels": {"reads": [{"table": "location", "columns": ["investor", "num_volunteers"]}], "writes": [{"table": "ads.ads_clicks_delta", "columns": ["investor", "num_volunteers"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO museums SELECT party_phone, policyholderid, environmental_impact_score FROM donationhistory WHERE party_phone > 371\"\n", "labels": {"reads": [{"table": "donationhistory", "columns": ["party_phone", "policyholderid", "environmental_impact_score"]}], "writes": [{"table": "museums", "columns": ["party_phone", "policyholderid", "environmental_impact_score"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO militaryinnovations SELECT capacity, established_date FROM weather WHERE capacity > 231;\nEOF\n", "labels": {"reads": [{"table": "weather", "columns": ["capacity", "established_date"]}], "writes": [{"table": "militaryinnovations", "columns": ["capacity", "established_date"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO gene SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\nexport TZ=Asia/Shanghai\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO school_enrollment SELECT * FROM legacy\ncur.execute(\"SELECT event_id, last_checkup_date FROM invoice LIMIT 4\")\n", "labels": {"reads": [{"table": "invoice", "columns": ["event_id", "last_checkup_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM securityincidents\", conn)\ndf.to_sql(\"dw.dw_campaigns_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "securityincidents", "columns": null}], "writes": [{"table": "dw.dw_campaigns_di", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"satellites_by_country\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"purchases\")\n", "labels": {"reads": [{"table": "satellites_by_country", "columns": null}], "writes": [{"table": "purchases", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model india_ingredient_sourcing depends on wellbeing_programs\ndbt build --models india_ingredient_sourcing --vars '{\"source_table\":\"wellbeing_programs\"}'\n", "labels": {"reads": [{"table": "wellbeing_programs", "columns": null}], "writes": [{"table": "india_ingredient_sourcing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO procedures SELECT * FROM legacy\ncur.execute(\"SELECT transportation_method, form_name FROM restaurant LIMIT 168\")\n", "labels": {"reads": [{"table": "restaurant", "columns": ["transportation_method", "form_name"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"eco_hotels\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "eco_hotels", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT inspection_date, heart_rate FROM arctic_research LIMIT 157\")\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO community_members SELECT occupancy_rate, council_tax_id, race, exhibitionid FROM financialwellbeing WHERE occupancy_rate > 113\")\n", "labels": {"reads": [{"table": "arctic_research", "columns": ["inspection_date", "heart_rate"]}, {"table": "financialwellbeing", "columns": ["occupancy_rate", "council_tax_id", "race", "exhibitionid"]}], "writes": [{"table": "community_members", "columns": ["occupancy_rate", "council_tax_id", "race", "exhibitionid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_transportation\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "urban_transportation", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT hispanic, business_size FROM legislation LIMIT 94;\nINSERT INTO journal_committee SELECT society, songid, recycler_id FROM seasonalvegetables WHERE society > 420;\nSQL\n", "labels": {"reads": [{"table": "legislation", "columns": ["hispanic", "business_size"]}, {"table": "seasonalvegetables", "columns": ["society", "songid", "recycler_id"]}], "writes": [{"table": "journal_committee", "columns": ["society", "songid", "recycler_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM performance_scores\"\n", "labels": {"reads": [{"table": "performance_scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT menuname, points FROM online_platform LIMIT 292;\nINSERT INTO membership_data SELECT asset_id, devices, resource_type, customerid FROM trip WHERE asset_id > 176;\nSQL\n", "labels": {"reads": [{"table": "online_platform", "columns": ["menuname", "points"]}, {"table": "trip", "columns": ["asset_id", "devices", "resource_type", "customerid"]}], "writes": [{"table": "membership_data", "columns": ["asset_id", "devices", "resource_type", "customerid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"marine_life_research\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"date\")\n", "labels": {"reads": [{"table": "marine_life_research", "columns": null}], "writes": [{"table": "date", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.denomination > 376).all()\n# src table: militarypatents\nengine.execute(\"INSERT INTO manufacturingplants SELECT * FROM militarypatents\")\n", "labels": {"reads": [{"table": "militarypatents", "columns": null}], "writes": [{"table": "manufacturingplants", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"organization\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"clinics\")\n", "labels": {"reads": [{"table": "organization", "columns": null}], "writes": [{"table": "clinics", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO esportsevents SELECT a.winery, b.provider_parity_score FROM bikerental a JOIN voting_data b ON a.donor = b.donor\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bikerental", "columns": null}, {"table": "voting_data", "columns": null}], "writes": [{"table": "esportsevents", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"traffic_citations\")\nsrc.write.insertInto(\"districts\", overwrite=True)\n", "labels": {"reads": [{"table": "traffic_citations", "columns": null}], "writes": [{"table": "districts", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO recycling_centers SELECT donorid, amount_used FROM investment_rounds WHERE donorid > 354\"\n", "labels": {"reads": [{"table": "investment_rounds", "columns": ["donorid", "amount_used"]}], "writes": [{"table": "recycling_centers", "columns": ["donorid", "amount_used"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"list\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"hotel_chains\")\n", "labels": {"reads": [{"table": "list", "columns": null}], "writes": [{"table": "hotel_chains", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO dws.shipments_daily SELECT organic, mappingid, cultural_diversity, cell_mobile_number FROM criticalincidents WHERE organic > 47\"\n", "labels": {"reads": [{"table": "criticalincidents", "columns": ["organic", "mappingid", "cultural_diversity", "cell_mobile_number"]}], "writes": [{"table": "dws.shipments_daily", "columns": ["organic", "mappingid", "cultural_diversity", "cell_mobile_number"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO marine_mammals SELECT detection_date, allocation_date, coach_id, artpiecename FROM fault_log_parts WHERE detection_date > 4\"\n", "labels": {"reads": [{"table": "fault_log_parts", "columns": ["detection_date", "allocation_date", "coach_id", "artpiecename"]}], "writes": [{"table": "marine_mammals", "columns": ["detection_date", "allocation_date", "coach_id", "artpiecename"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.updated_at > 13).all()\n# src table: precipitation_data\nengine.execute(\"INSERT INTO dws_events_di SELECT * FROM precipitation_data\")\n", "labels": {"reads": [{"table": "precipitation_data", "columns": null}], "writes": [{"table": "dws_events_di", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bi.bi_orders_hourly\").toPandas()\ndf[[\"num_attendees\", \"funding_received\"]].to_sql(\"document_types\", engine, index=False)\n", "labels": {"reads": [{"table": "bi.bi_orders_hourly", "columns": null}], "writes": [{"table": "document_types", "columns": ["num_attendees", "funding_received"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO labor_unions SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"investments_esg\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "investments_esg", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT donor_category, company_gender FROM veteran_stats LIMIT 145\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "veteran_stats", "columns": ["donor_category", "company_gender"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO enrolled_in SELECT shipping_agent_name, journalist_id, injury FROM tracklists WHERE shipping_agent_name > 229\"\n", "labels": {"reads": [{"table": "tracklists", "columns": ["shipping_agent_name", "journalist_id", "injury"]}], "writes": [{"table": "enrolled_in", "columns": ["shipping_agent_name", "journalist_id", "injury"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO participants SELECT 1\"\nRETRIES=${RETRIES:-3}\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO suppliersfairlabor SELECT * FROM legacy\ncur.execute(\"SELECT grant_end_date, premises_type FROM follows LIMIT 6\")\n", "labels": {"reads": [{"table": "follows", "columns": ["grant_end_date", "premises_type"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ca_menu_items SELECT employee_count, max_depth, mission_id, common_name FROM legislation WHERE employee_count > 248\"\n", "labels": {"reads": [{"table": "legislation", "columns": ["employee_count", "max_depth", "mission_id", "common_name"]}], "writes": [{"table": "ca_menu_items", "columns": ["employee_count", "max_depth", "mission_id", "common_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dysprosium_mines\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dysprosium_mines", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nhive -e \"INSERT INTO phone_market SELECT days_held, black FROM co2_sequestration WHERE days_held > 190\"\n", "labels": {"reads": [{"table": "co2_sequestration", "columns": ["days_held", "black"]}], "writes": [{"table": "phone_market", "columns": ["days_held", "black"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO savings_programs SELECT a.channel, b.cust_name FROM wins a JOIN instructors b ON a.yield = b.yield\"\n", "labels": {"reads": [{"table": "wins", "columns": null}, {"table": "instructors", "columns": null}], "writes": [{"table": "savings_programs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO volunteerhours SELECT a.amenid, b.operation_count FROM ucl_top10 a JOIN stg.refunds_hourly b ON a.src_apid = b.src_apid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ucl_top10", "columns": null}, {"table": "stg.refunds_hourly", "columns": null}], "writes": [{"table": "volunteerhours", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT community_center_id, dlocation FROM customer_address_history\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"dwd_events_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "customer_address_history", "columns": ["community_center_id", "dlocation"]}], "writes": [{"table": "dwd_events_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT address, count_time FROM course_authors_and_tutors\", engine)\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"orgdonations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "course_authors_and_tutors", "columns": ["address", "count_time"]}], "writes": [{"table": "orgdonations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO intelligence_agents SELECT resource, menuitem FROM adaptation_projects WHERE resource > 178\"], check=True)\n", "labels": {"reads": [{"table": "adaptation_projects", "columns": ["resource", "menuitem"]}], "writes": [{"table": "intelligence_agents", "columns": ["resource", "menuitem"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"volunteer_hours\").toPandas()\ndf[[\"visit_month\", \"order_details\"]].to_sql(\"teams_mascots\", engine, index=False)\n", "labels": {"reads": [{"table": "volunteer_hours", "columns": null}], "writes": [{"table": "teams_mascots", "columns": ["visit_month", "order_details"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO claims SELECT venue, founder_lgbtq, roomname, union_member FROM licenses WHERE venue > 486\"\n", "labels": {"reads": [{"table": "licenses", "columns": ["venue", "founder_lgbtq", "roomname", "union_member"]}], "writes": [{"table": "claims", "columns": ["venue", "founder_lgbtq", "roomname", "union_member"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT center, document_id FROM exhibitiondetails LIMIT 5;\nINSERT INTO support_tickets SELECT retailer, star_rating_description FROM iron WHERE retailer > 207;\nSQL\n", "labels": {"reads": [{"table": "exhibitiondetails", "columns": ["center", "document_id"]}, {"table": "iron", "columns": ["retailer", "star_rating_description"]}], "writes": [{"table": "support_tickets", "columns": ["retailer", "star_rating_description"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM crops\"\n", "labels": {"reads": [{"table": "crops", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cybersecurity.strategies\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"undergoes\")\n", "labels": {"reads": [{"table": "cybersecurity.strategies", "columns": null}], "writes": [{"table": "undergoes", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"well_production\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "well_production", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO station_crime_rates SELECT drug_id, num_accessible_tech_centers FROM service_budget WHERE drug_id > 257\"\n", "labels": {"reads": [{"table": "service_budget", "columns": ["drug_id", "num_accessible_tech_centers"]}], "writes": [{"table": "station_crime_rates", "columns": ["drug_id", "num_accessible_tech_centers"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO attendee_demographics (pet_age, daily_distance) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "attendee_demographics", "columns": ["pet_age", "daily_distance"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.painting_name > 205).all()\n# src table: france_culture\nengine.execute(\"INSERT INTO courts SELECT * FROM france_culture\")\n", "labels": {"reads": [{"table": "france_culture", "columns": null}], "writes": [{"table": "courts", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO students_enrollment SELECT * FROM legacy\ncur.execute(\"SELECT digital_asset, grant_end_date FROM financialwellbeing LIMIT 263\")\n", "labels": {"reads": [{"table": "financialwellbeing", "columns": ["digital_asset", "grant_end_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ocean_floor SELECT 1\"\nset -euo pipefail\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT weeks_on_top, baseprice FROM oil_production LIMIT 83;\nINSERT INTO ads.ads_payments_hourly SELECT safety_rating, species FROM manufacturer WHERE safety_rating > 303;\nSQL\n", "labels": {"reads": [{"table": "oil_production", "columns": ["weeks_on_top", "baseprice"]}, {"table": "manufacturer", "columns": ["safety_rating", "species"]}], "writes": [{"table": "ads.ads_payments_hourly", "columns": ["safety_rating", "species"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ota_name > 277).all()\n# src table: ticket_sales\nengine.execute(\"INSERT INTO hospitallocations SELECT * FROM ticket_sales\")\n", "labels": {"reads": [{"table": "ticket_sales", "columns": null}], "writes": [{"table": "hospitallocations", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO departments SELECT course_completion, request, dribbling FROM location WHERE course_completion > 247\"\n", "labels": {"reads": [{"table": "location", "columns": ["course_completion", "request", "dribbling"]}], "writes": [{"table": "departments", "columns": ["course_completion", "request", "dribbling"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"bridge\").toPandas()\ndf[[\"strain_name\", \"share_count\"]].to_sql(\"members\", engine, index=False)\n", "labels": {"reads": [{"table": "bridge", "columns": null}], "writes": [{"table": "members", "columns": ["strain_name", "share_count"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT ironid, accommodation_type FROM cargos\", engine)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"financial_capability_id\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "cargos", "columns": ["ironid", "accommodation_type"]}], "writes": [{"table": "financial_capability_id", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stores (song_name, trade_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stores", "columns": ["song_name", "trade_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nsql = \"INSERT INTO ticketsales SELECT a.hospitalid, b.year_opened FROM restaurants_tx a JOIN navalequipmentmaintenance b ON a.individual_id = b.individual_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "restaurants_tx", "columns": null}, {"table": "navalequipmentmaintenance", "columns": null}], "writes": [{"table": "ticketsales", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO brands SELECT campaign_name, policyholderid, catalog_entry_name FROM city_tech WHERE campaign_name > 193\")\n", "labels": {"reads": [{"table": "city_tech", "columns": ["campaign_name", "policyholderid", "catalog_entry_name"]}], "writes": [{"table": "brands", "columns": ["campaign_name", "policyholderid", "catalog_entry_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.clicks_df (engagementid, headquarters) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.clicks_df", "columns": ["engagementid", "headquarters"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO container_ships (individual_first_name, shipment_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "container_ships", "columns": ["individual_first_name", "shipment_date"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO clinics_sa SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT eco_certified, restypename FROM store_product\", engine)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"vr_tech\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "store_product", "columns": ["eco_certified", "restypename"]}], "writes": [{"table": "vr_tech", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM employment\"\n", "labels": {"reads": [{"table": "employment", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_inventory SELECT a.subject_area_id, b.doctor_id FROM dw.dw_coupon_use_daily a JOIN timbersales b ON a.archaeologistid = b.archaeologistid\"\n", "labels": {"reads": [{"table": "dw.dw_coupon_use_daily", "columns": null}, {"table": "timbersales", "columns": null}], "writes": [{"table": "bi.bi_inventory", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO donationsbycause SELECT enable_location_tracking, game_count FROM station_emergencies WHERE enable_location_tracking > 458\"], check=True)\n", "labels": {"reads": [{"table": "station_emergencies", "columns": ["enable_location_tracking", "game_count"]}], "writes": [{"table": "donationsbycause", "columns": ["enable_location_tracking", "game_count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hireyear > 488).all()\n# src table: bi.users_full\nengine.execute(\"INSERT INTO mart.vendors_full SELECT * FROM bi.users_full\")\n", "labels": {"reads": [{"table": "bi.users_full", "columns": null}], "writes": [{"table": "mart.vendors_full", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO transport SELECT a.court_appearances, b.driver_id FROM haircare_cruelty a JOIN sustainable_projects b ON a.total_horses = b.total_horses\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "haircare_cruelty", "columns": null}, {"table": "sustainable_projects", "columns": null}], "writes": [{"table": "transport", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO donationsbycause SELECT gtype, eventtype, ei_category, frameworkcountry FROM workshops WHERE gtype > 285;\nEOF\n", "labels": {"reads": [{"table": "workshops", "columns": ["gtype", "eventtype", "ei_category", "frameworkcountry"]}], "writes": [{"table": "donationsbycause", "columns": ["gtype", "eventtype", "ei_category", "frameworkcountry"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT menuitemname, draft_details FROM route LIMIT 93;\nINSERT INTO menu_vendors SELECT departure_date, fault_short_name, part_fault_id, hiv FROM contracts WHERE departure_date > 345;\nSQL\n", "labels": {"reads": [{"table": "route", "columns": ["menuitemname", "draft_details"]}, {"table": "contracts", "columns": ["departure_date", "fault_short_name", "part_fault_id", "hiv"]}], "writes": [{"table": "menu_vendors", "columns": ["departure_date", "fault_short_name", "part_fault_id", "hiv"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM discount_coupons\", conn)\ndf.to_sql(\"communityhealthworkerscanada\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "discount_coupons", "columns": null}], "writes": [{"table": "communityhealthworkerscanada", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO water_usage SELECT a.timestamp, b.bdate FROM bi.bi_inventory_di a JOIN customers_cards b ON a.is_autonomous = b.is_autonomous\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "bi.bi_inventory_di", "columns": null}, {"table": "customers_cards", "columns": null}], "writes": [{"table": "water_usage", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city.community_policing\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"areas\")\n", "labels": {"reads": [{"table": "city.community_policing", "columns": null}], "writes": [{"table": "areas", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT base_id, mean_temperature_f FROM economic_diversification_efforts LIMIT 428\")\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO dw.dw_member_point_hourly SELECT reo_type, fueldate, cust_name FROM regulatory_frameworks WHERE reo_type > 218\")\n", "labels": {"reads": [{"table": "economic_diversification_efforts", "columns": ["base_id", "mean_temperature_f"]}, {"table": "regulatory_frameworks", "columns": ["reo_type", "fueldate", "cust_name"]}], "writes": [{"table": "dw.dw_member_point_hourly", "columns": ["reo_type", "fueldate", "cust_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO cases SELECT a.astronaut_id, b.sculpture_name FROM trainmaintenance a JOIN esports_teams b ON a.latitude = b.latitude\"\n", "labels": {"reads": [{"table": "trainmaintenance", "columns": null}, {"table": "esports_teams", "columns": null}], "writes": [{"table": "cases", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO financial_transactions SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO invoice_lines SELECT workoutid, sd_id, join_year, user_name FROM dws.dws_orders_full WHERE workoutid > 485\"\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": ["workoutid", "sd_id", "join_year", "user_name"]}], "writes": [{"table": "invoice_lines", "columns": ["workoutid", "sd_id", "join_year", "user_name"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mappinglengths\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mappinglengths", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.mart_users_delta SELECT languages, location_name FROM news_views WHERE languages > 193\"], check=True)\n", "labels": {"reads": [{"table": "news_views", "columns": ["languages", "location_name"]}], "writes": [{"table": "mart.mart_users_delta", "columns": ["languages", "location_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO vehicledata SELECT master_customer_id, cultural_significance, part_fault_id, points_per_game FROM sustainable_urban WHERE master_customer_id > 254\"\n", "labels": {"reads": [{"table": "sustainable_urban", "columns": ["master_customer_id", "cultural_significance", "part_fault_id", "points_per_game"]}], "writes": [{"table": "vehicledata", "columns": ["master_customer_id", "cultural_significance", "part_fault_id", "points_per_game"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT train_number, observation_id FROM smartcontracts LIMIT 340\")\nimport logging\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO militaryinnovations SELECT mailing_date, job_id, color, attorney_id FROM fundings WHERE mailing_date > 263\")\n", "labels": {"reads": [{"table": "smartcontracts", "columns": ["train_number", "observation_id"]}, {"table": "fundings", "columns": ["mailing_date", "job_id", "color", "attorney_id"]}], "writes": [{"table": "militaryinnovations", "columns": ["mailing_date", "job_id", "color", "attorney_id"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO program_budget SELECT 1\"\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table florida_conservation_initiatives --target-dir /tmp/land\n", "labels": {"reads": [{"table": "florida_conservation_initiatives", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO voting_data SELECT change_date, date_of_notes, served_subscribers FROM mart_shipments_full WHERE change_date > 413\"\n", "labels": {"reads": [{"table": "mart_shipments_full", "columns": ["change_date", "date_of_notes", "served_subscribers"]}], "writes": [{"table": "voting_data", "columns": ["change_date", "date_of_notes", "served_subscribers"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM reverselogisticstransactions\"\n", "labels": {"reads": [{"table": "reverselogisticstransactions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"wastewater_plants\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ods.shipments_df\")\n", "labels": {"reads": [{"table": "wastewater_plants", "columns": null}], "writes": [{"table": "ods.shipments_df", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO appliances SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO ads.ads_cart_item_hourly (artwork, success) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "ads.ads_cart_item_hourly", "columns": ["artwork", "success"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO recyclingcenters SELECT 1\"\nRETRIES=${RETRIES:-3}\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO inspections SELECT maintenance_id, working_year_starts FROM tracks WHERE maintenance_id > 55\"\n", "labels": {"reads": [{"table": "tracks", "columns": ["maintenance_id", "working_year_starts"]}], "writes": [{"table": "inspections", "columns": ["maintenance_id", "working_year_starts"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table renewable_projects --columns sanctuary,vessel --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "renewable_projects", "columns": ["sanctuary", "vessel"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_frame(ctx, \"store_product\")\npersist_to_store(df, \"military_equipment\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "store_product", "columns": null}], "writes": [{"table": "military_equipment", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"budget_allocations\")\nsink_to_store(df, \"thefttypes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "budget_allocations", "columns": null}], "writes": [{"table": "thefttypes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\nmkdir -p /tmp/joblog\nsqoop import --connect \"$JDBC\" --table ods.products_hourly --target-dir /tmp/land\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO restaurant SELECT a.invoice_date, b.business_id FROM global_sales_2022 a JOIN mart.mart_events_di b ON a.forest_id = b.forest_id\"\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}, {"table": "mart.mart_events_di", "columns": null}], "writes": [{"table": "restaurant", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table african_tourism --columns typical_buying_price,decision --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "african_tourism", "columns": ["typical_buying_price", "decision"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dailystreams SELECT booking_id, waste_type, follow_up_date, functional_area_code FROM bi.bi_risk_score_delta WHERE booking_id > 8\"], check=True)\n", "labels": {"reads": [{"table": "bi.bi_risk_score_delta", "columns": ["booking_id", "waste_type", "follow_up_date", "functional_area_code"]}], "writes": [{"table": "dailystreams", "columns": ["booking_id", "waste_type", "follow_up_date", "functional_area_code"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ads_coupon_use_full SELECT permitid, rainfall, matchid FROM eco_materials WHERE permitid > 490\")\n", "labels": {"reads": [{"table": "eco_materials", "columns": ["permitid", "rainfall", "matchid"]}], "writes": [{"table": "ads_coupon_use_full", "columns": ["permitid", "rainfall", "matchid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"strainlabresults\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "strainlabresults", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO mart.mart_sessions_di SELECT classroom, inclusive_housing_policy, founded, nominee FROM co2emissions WHERE classroom > 49\"\n", "labels": {"reads": [{"table": "co2emissions", "columns": ["classroom", "inclusive_housing_policy", "founded", "nominee"]}], "writes": [{"table": "mart.mart_sessions_di", "columns": ["classroom", "inclusive_housing_policy", "founded", "nominee"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO carbon_prices_3 SELECT diagnosis, characteristic_data_type FROM astronaut_missions WHERE diagnosis > 408\"\n", "labels": {"reads": [{"table": "astronaut_missions", "columns": ["diagnosis", "characteristic_data_type"]}], "writes": [{"table": "carbon_prices_3", "columns": ["diagnosis", "characteristic_data_type"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.cancel_date > 362).all()\n# src table: fruitimport\nengine.execute(\"INSERT INTO researchgrants SELECT * FROM fruitimport\")\n", "labels": {"reads": [{"table": "fruitimport", "columns": null}], "writes": [{"table": "researchgrants", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = fetch_source(ctx, \"submersible_dives\")\npersist_to_target(df, \"cultural_competency_training\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "submersible_dives", "columns": null}], "writes": [{"table": "cultural_competency_training", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.clicks > 343).all()\n# src table: military_bases\nengine.execute(\"INSERT INTO venture SELECT * FROM military_bases\")\n", "labels": {"reads": [{"table": "military_bases", "columns": null}], "writes": [{"table": "venture", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table mental_health_scores --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mental_health_scores", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mart.mart_products_df SELECT staff_name, label_id, usage, emp_num FROM creativeais WHERE staff_name > 267;\nEOF\n", "labels": {"reads": [{"table": "creativeais", "columns": ["staff_name", "label_id", "usage", "emp_num"]}], "writes": [{"table": "mart.mart_products_df", "columns": ["staff_name", "label_id", "usage", "emp_num"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model military_personnel depends on overwatch_scores\ndbt build -s military_personnel --vars 'source: overwatch_scores'\n", "labels": {"reads": [{"table": "overwatch_scores", "columns": null}], "writes": [{"table": "military_personnel", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.ll_id > 390).all()\n# src table: stg.stg_clicks_delta\nengine.execute(\"INSERT INTO fireincidents SELECT * FROM stg.stg_clicks_delta\")\n", "labels": {"reads": [{"table": "stg.stg_clicks_delta", "columns": null}], "writes": [{"table": "fireincidents", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"field\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"mart.vendors_full\")\n", "labels": {"reads": [{"table": "field", "columns": null}], "writes": [{"table": "mart.vendors_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table light_rail_lines --columns catalog_level_number,elevation --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "light_rail_lines", "columns": ["catalog_level_number", "elevation"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO daily_oil_production SELECT ticketprice, fanid FROM socialimpactinvestments WHERE ticketprice > 357\"], check=True)\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": ["ticketprice", "fanid"]}], "writes": [{"table": "daily_oil_production", "columns": ["ticketprice", "fanid"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tours SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model astronauts depends on ods.ods_clicks_di\ndbt run --models astronauts --vars '{\"src\":\"ods.ods_clicks_di\"}'\n", "labels": {"reads": [{"table": "ods.ods_clicks_di", "columns": null}], "writes": [{"table": "astronauts", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM articles_es\", conn)\ndf.to_sql(\"regular_order_products\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "articles_es", "columns": null}], "writes": [{"table": "regular_order_products", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.other_account_details > 361).all()\n# src table: socially_responsible_lending\nengine.execute(\"INSERT INTO sports_events SELECT * FROM socially_responsible_lending\")\n", "labels": {"reads": [{"table": "socially_responsible_lending", "columns": null}], "writes": [{"table": "sports_events", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT projectid, org_id FROM concert_events\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\ndf.to_sql(\"ingredientsvegancrueltyfree\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "concert_events", "columns": ["projectid", "org_id"]}], "writes": [{"table": "ingredientsvegancrueltyfree", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.mart_payments_delta\", conn)\ndf.to_sql(\"bi.inventory_daily\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_payments_delta", "columns": null}], "writes": [{"table": "bi.inventory_daily", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO animal_population SELECT a.last_maintenance_date, b.donorname FROM fairness_scores a JOIN justice_schemas.legal_tech_providers b ON a.artistid = b.artistid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "fairness_scores", "columns": null}, {"table": "justice_schemas.legal_tech_providers", "columns": null}], "writes": [{"table": "animal_population", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT data_usage, tx_id FROM spacecraftmanufacturing LIMIT 321\")\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO nutrition_facts SELECT album, coalid, author_community, astronaut FROM sustainable_tourism_practices WHERE album > 151\")\n", "labels": {"reads": [{"table": "spacecraftmanufacturing", "columns": ["data_usage", "tx_id"]}, {"table": "sustainable_tourism_practices", "columns": ["album", "coalid", "author_community", "astronaut"]}], "writes": [{"table": "nutrition_facts", "columns": ["album", "coalid", "author_community", "astronaut"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mine\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"average\")\n", "labels": {"reads": [{"table": "mine", "columns": null}], "writes": [{"table": "average", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table problem_log --target-dir /tmp/land\n", "labels": {"reads": [{"table": "problem_log", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO companies (length_meters, comments) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "companies", "columns": ["length_meters", "comments"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO bi.coupon_use SELECT 1\"\nRETRIES=${RETRIES:-3}\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_users_full\").toPandas()\ndf[[\"contract_start_date\", \"enrollment_date\"]].to_sql(\"humanitarianmissions\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_users_full", "columns": null}], "writes": [{"table": "humanitarianmissions", "columns": ["contract_start_date", "enrollment_date"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model climate_projects depends on workforce_development\ndbt run --models climate_projects --vars 'source: workforce_development'\n", "labels": {"reads": [{"table": "workforce_development", "columns": null}], "writes": [{"table": "climate_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT birth_country, ai_powered_features FROM forms\", engine)\nimport logging\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"mart.mart_users_delta\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "forms", "columns": ["birth_country", "ai_powered_features"]}], "writes": [{"table": "mart.mart_users_delta", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"marine_species_observations\")\nsrc.write.insertInto(\"loan\", overwrite=True)\n", "labels": {"reads": [{"table": "marine_species_observations", "columns": null}], "writes": [{"table": "loan", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO renewable_energy_projects SELECT 1\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO communities SELECT advisor, assignment_date, training_id, spill_name FROM daily_oil_production WHERE advisor > 274\"\n", "labels": {"reads": [{"table": "daily_oil_production", "columns": ["advisor", "assignment_date", "training_id", "spill_name"]}], "writes": [{"table": "communities", "columns": ["advisor", "assignment_date", "training_id", "spill_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO bi.bi_risk_score_full SELECT resource_type, labor_hour_id FROM latam_schema.education_budget WHERE resource_type > 304\"\n", "labels": {"reads": [{"table": "latam_schema.education_budget", "columns": ["resource_type", "labor_hour_id"]}], "writes": [{"table": "bi.bi_risk_score_full", "columns": ["resource_type", "labor_hour_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO hydro_power (ship_id, min_temperature_f) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "hydro_power", "columns": ["ship_id", "min_temperature_f"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO ads_vendors_hourly (training_name, nationality) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "ads_vendors_hourly", "columns": ["training_name", "nationality"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\nhive -e \"INSERT INTO virtual_tour_stats SELECT grade, role_description FROM student_courses WHERE grade > 219\"\n", "labels": {"reads": [{"table": "student_courses", "columns": ["grade", "role_description"]}], "writes": [{"table": "virtual_tour_stats", "columns": ["grade", "role_description"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO vehicle_safety_testing SELECT exhibitioncountry, productcategory FROM ods_shipments_df WHERE exhibitioncountry > 107\"], check=True)\n", "labels": {"reads": [{"table": "ods_shipments_df", "columns": ["exhibitioncountry", "productcategory"]}], "writes": [{"table": "vehicle_safety_testing", "columns": ["exhibitioncountry", "productcategory"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"digital_divide_initiatives\")\nwrite_to_output(df, \"state_contracts\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": null}], "writes": [{"table": "state_contracts", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"victims\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "victims", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO monthly_temp SELECT * FROM legacy\ncur.execute(\"SELECT grade, industry_4_0 FROM exhibition_visitors LIMIT 43\")\n", "labels": {"reads": [{"table": "exhibition_visitors", "columns": ["grade", "industry_4_0"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hourlyrate, max_dew_point_f FROM militarypersonnel LIMIT 195\")\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO projecttimelinebybudget SELECT wildlife_type_id, is_autonomous, employmentdate, minename FROM contract_states WHERE wildlife_type_id > 165\")\n", "labels": {"reads": [{"table": "militarypersonnel", "columns": ["hourlyrate", "max_dew_point_f"]}, {"table": "contract_states", "columns": ["wildlife_type_id", "is_autonomous", "employmentdate", "minename"]}], "writes": [{"table": "projecttimelinebybudget", "columns": ["wildlife_type_id", "is_autonomous", "employmentdate", "minename"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"socialimpactinvestments\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"pollution_initiatives\")\n", "labels": {"reads": [{"table": "socialimpactinvestments", "columns": null}], "writes": [{"table": "pollution_initiatives", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO savings SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO midwest_region SELECT registered_date, rec_engine, lender_id, user_id FROM space_missions_2 WHERE registered_date > 188\"\n", "labels": {"reads": [{"table": "space_missions_2", "columns": ["registered_date", "rec_engine", "lender_id", "user_id"]}], "writes": [{"table": "midwest_region", "columns": ["registered_date", "rec_engine", "lender_id", "user_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"art_pieces\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"heart_rate_data\")\n", "labels": {"reads": [{"table": "art_pieces", "columns": null}], "writes": [{"table": "heart_rate_data", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM safety_incident\"\n", "labels": {"reads": [{"table": "safety_incident", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model road_construction depends on policy\ndbt build -s road_construction --vars '{\"src\":\"policy\"}'\n", "labels": {"reads": [{"table": "policy", "columns": null}], "writes": [{"table": "road_construction", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO species_observations SELECT 1\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainability_metrics SELECT athlete_id, trainingdate FROM storage_tech WHERE athlete_id > 252\"\n", "labels": {"reads": [{"table": "storage_tech", "columns": ["athlete_id", "trainingdate"]}], "writes": [{"table": "sustainability_metrics", "columns": ["athlete_id", "trainingdate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rnd_budget\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "rnd_budget", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_input(ctx, \"infantmortalitydata\")\nsink_to_output(df, \"educators\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "infantmortalitydata", "columns": null}], "writes": [{"table": "educators", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"people_addresses\")\npersist_to_warehouse(df, \"communityhealthworkerscanada\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "people_addresses", "columns": null}], "writes": [{"table": "communityhealthworkerscanada", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nRETRIES=${RETRIES:-3}\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table dw.member_point_daily --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dw.member_point_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods.products_hourly SELECT a.booking_id, b.complaintid FROM workplaces a JOIN person b ON a.calendar = b.calendar\"\n", "labels": {"reads": [{"table": "workplaces", "columns": null}, {"table": "person", "columns": null}], "writes": [{"table": "ods.products_hourly", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO media_library SELECT people_id, license_id, dst_apid, destruction_authorised_by_employee_id FROM ads_coupon_use_full WHERE people_id > 474\"], check=True)\n", "labels": {"reads": [{"table": "ads_coupon_use_full", "columns": ["people_id", "license_id", "dst_apid", "destruction_authorised_by_employee_id"]}], "writes": [{"table": "media_library", "columns": ["people_id", "license_id", "dst_apid", "destruction_authorised_by_employee_id"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO malicious_activity SELECT training_name, fan_age FROM ads.ads_orders WHERE training_name > 153\")\n", "labels": {"reads": [{"table": "ads.ads_orders", "columns": ["training_name", "fan_age"]}], "writes": [{"table": "malicious_activity", "columns": ["training_name", "fan_age"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO pharmasales SELECT max_gust_speed_mph, rows, incident_type FROM ods.ods_risk_score_full WHERE max_gust_speed_mph > 65\")\n", "labels": {"reads": [{"table": "ods.ods_risk_score_full", "columns": ["max_gust_speed_mph", "rows", "incident_type"]}], "writes": [{"table": "pharmasales", "columns": ["max_gust_speed_mph", "rows", "incident_type"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.payments_daily\", conn)\ndf.to_sql(\"research_vessels\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.payments_daily", "columns": null}], "writes": [{"table": "research_vessels", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT primaryaffiliation, unit_id FROM environmentalimpact LIMIT 195\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nimport logging\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "environmentalimpact", "columns": ["primaryaffiliation", "unit_id"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"has_allergy\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"farm\")\n", "labels": {"reads": [{"table": "has_allergy", "columns": null}], "writes": [{"table": "farm", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.build_year > 455).all()\n# src table: researchgrants\nengine.execute(\"INSERT INTO bi.events_df SELECT * FROM researchgrants\")\n", "labels": {"reads": [{"table": "researchgrants", "columns": null}], "writes": [{"table": "bi.events_df", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO bi.bi_inventory SELECT a.discovered_date, b.method_id FROM graduates a JOIN community_engagement b ON a.points_per_game = b.points_per_game\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "graduates", "columns": null}, {"table": "community_engagement", "columns": null}], "writes": [{"table": "bi.bi_inventory", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"territory.human_rights_data\")\nsrc.write.insertInto(\"economic_diversification_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "territory.human_rights_data", "columns": null}], "writes": [{"table": "economic_diversification_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_dataset(ctx, \"tourist_attraction_features\")\nwrite_to_target(df, \"france_culture\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "tourist_attraction_features", "columns": null}], "writes": [{"table": "france_culture", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model militaryequipmentsales depends on student_lifelong_learning\ndbt run --models militaryequipmentsales --vars '{\"src\":\"student_lifelong_learning\"}'\n", "labels": {"reads": [{"table": "student_lifelong_learning", "columns": null}], "writes": [{"table": "militaryequipmentsales", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_agriculture_initiatives\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ticketspending\")\n", "labels": {"reads": [{"table": "urban_agriculture_initiatives", "columns": null}], "writes": [{"table": "ticketspending", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws.cart_item_di (branch_id, regulation) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws.cart_item_di", "columns": ["branch_id", "regulation"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO program_outcomes SELECT genre, equipment_id FROM space_missions WHERE genre > 458\"\n", "labels": {"reads": [{"table": "space_missions", "columns": ["genre", "equipment_id"]}], "writes": [{"table": "program_outcomes", "columns": ["genre", "equipment_id"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT time_day, mealname FROM social_issues LIMIT 139;\nINSERT INTO artifactanalysis SELECT well_name, number_thousands FROM inmates WHERE well_name > 405;\nSQL\n", "labels": {"reads": [{"table": "social_issues", "columns": ["time_day", "mealname"]}, {"table": "inmates", "columns": ["well_name", "number_thousands"]}], "writes": [{"table": "artifactanalysis", "columns": ["well_name", "number_thousands"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT lesson_status_code, weeks_on_top FROM digital_divide_initiatives LIMIT 52;\nINSERT INTO dwd.dwd_vendors SELECT shipment_date, hours_contributed, half FROM stg.stg_events_hourly WHERE shipment_date > 315;\nSQL\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": ["lesson_status_code", "weeks_on_top"]}, {"table": "stg.stg_events_hourly", "columns": ["shipment_date", "hours_contributed", "half"]}], "writes": [{"table": "dwd.dwd_vendors", "columns": ["shipment_date", "hours_contributed", "half"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO forest_species SELECT a.acc_bal, b.eventdate FROM mental_health_parity a JOIN ads.ads_shipments_delta b ON a.shop_details = b.shop_details\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "mental_health_parity", "columns": null}, {"table": "ads.ads_shipments_delta", "columns": null}], "writes": [{"table": "forest_species", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"drug_approval\")\nsrc.write.insertInto(\"agricultural_projects\", overwrite=True)\n", "labels": {"reads": [{"table": "drug_approval", "columns": null}], "writes": [{"table": "agricultural_projects", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_source(ctx, \"biomes\")\nsave_to_target(df, \"budget_allocations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "biomes", "columns": null}], "writes": [{"table": "budget_allocations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT number_of_sightings, record_id FROM electricvehiclestats LIMIT 253\")\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO policy_feedback SELECT violation_count, year_working, productionrate, max_dissolved_oxygen FROM people WHERE violation_count > 286\")\n", "labels": {"reads": [{"table": "electricvehiclestats", "columns": ["number_of_sightings", "record_id"]}, {"table": "people", "columns": ["violation_count", "year_working", "productionrate", "max_dissolved_oxygen"]}], "writes": [{"table": "policy_feedback", "columns": ["violation_count", "year_working", "productionrate", "max_dissolved_oxygen"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT digital_channel, region_id FROM stock_levels LIMIT 456;\nINSERT INTO dwd.vendors SELECT dept_code, savingsid, factory_name FROM farm WHERE dept_code > 357;\nSQL\n", "labels": {"reads": [{"table": "stock_levels", "columns": ["digital_channel", "region_id"]}, {"table": "farm", "columns": ["dept_code", "savingsid", "factory_name"]}], "writes": [{"table": "dwd.vendors", "columns": ["dept_code", "savingsid", "factory_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM heritage_sites\"\n", "labels": {"reads": [{"table": "heritage_sites", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"appointments\").toPandas()\ndf[[\"court_id\", \"storeid\"]].to_sql(\"government_transparency\", engine, index=False)\n", "labels": {"reads": [{"table": "appointments", "columns": null}], "writes": [{"table": "government_transparency", "columns": ["court_id", "storeid"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO product_ingredient SELECT a.use_date, b.fan_age FROM lots a JOIN exhibition_visits b ON a.ride_id = b.ride_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "lots", "columns": null}, {"table": "exhibition_visits", "columns": null}], "writes": [{"table": "product_ingredient", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO stars SELECT * FROM legacy\ncur.execute(\"SELECT fan_name, staff_address_id FROM all_programs LIMIT 300\")\n", "labels": {"reads": [{"table": "all_programs", "columns": ["fan_name", "staff_address_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM match_season\"\n", "labels": {"reads": [{"table": "match_season", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model spending depends on film_market_estimation\ndbt build --models spending --vars 'source: film_market_estimation'\n", "labels": {"reads": [{"table": "film_market_estimation", "columns": null}], "writes": [{"table": "spending", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.animal > 3).all()\n# src table: exam_results\nengine.execute(\"INSERT INTO carbonoffsetinitiatives SELECT * FROM exam_results\")\n", "labels": {"reads": [{"table": "exam_results", "columns": null}], "writes": [{"table": "carbonoffsetinitiatives", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model communitypolicing depends on jupiter_spacecraft\ndbt run -s communitypolicing --vars '{\"src\":\"jupiter_spacecraft\"}'\n", "labels": {"reads": [{"table": "jupiter_spacecraft", "columns": null}], "writes": [{"table": "communitypolicing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO excavations SELECT train_type, rental_rate FROM bi.bi_events_daily WHERE train_type > 471\"\n", "labels": {"reads": [{"table": "bi.bi_events_daily", "columns": ["train_type", "rental_rate"]}], "writes": [{"table": "excavations", "columns": ["train_type", "rental_rate"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table wellbeing_programs --columns projectname,date_complaint_raised --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "wellbeing_programs", "columns": ["projectname", "date_complaint_raised"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT preference_rating, source_system_code FROM sales_by_quarter LIMIT 299;\nINSERT INTO basketball_match SELECT participation_id, pediatrician_id, worker_id, name_full FROM artifacts WHERE participation_id > 476;\nSQL\n", "labels": {"reads": [{"table": "sales_by_quarter", "columns": ["preference_rating", "source_system_code"]}, {"table": "artifacts", "columns": ["participation_id", "pediatrician_id", "worker_id", "name_full"]}], "writes": [{"table": "basketball_match", "columns": ["participation_id", "pediatrician_id", "worker_id", "name_full"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dwd.dwd_exposure_full SELECT * FROM legacy\ncur.execute(\"SELECT operation_type, release_date FROM screen_mode LIMIT 201\")\n", "labels": {"reads": [{"table": "screen_mode", "columns": ["operation_type", "release_date"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"scientists\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "scientists", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table space_missions_2 --columns book_club_id,other_account_details --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "space_missions_2", "columns": ["book_club_id", "other_account_details"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO blockchain_tech SELECT blockfloor, participant_type_code, daily_visitors FROM ocean_acidity WHERE blockfloor > 184;\nEOF\n", "labels": {"reads": [{"table": "ocean_acidity", "columns": ["blockfloor", "participant_type_code", "daily_visitors"]}], "writes": [{"table": "blockchain_tech", "columns": ["blockfloor", "participant_type_code", "daily_visitors"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_table(ctx, \"military_tech\")\nupsert_to_output(df, \"police_stations\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "military_tech", "columns": null}], "writes": [{"table": "police_stations", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO mine SELECT team_name, scientist FROM ads.ads_risk_score_hourly WHERE team_name > 341;\nEOF\n", "labels": {"reads": [{"table": "ads.ads_risk_score_hourly", "columns": ["team_name", "scientist"]}], "writes": [{"table": "mine", "columns": ["team_name", "scientist"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM dws.dws_member_point_df\"\n", "labels": {"reads": [{"table": "dws.dws_member_point_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO exoplanet_discoveries (sales_channel, ship_name) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "exoplanet_discoveries", "columns": ["sales_channel", "ship_name"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT feedtype, application FROM attorney_billing_rates LIMIT 3;\nINSERT INTO investors SELECT product_type_code, stat_id FROM safetytesting WHERE product_type_code > 462;\nSQL\n", "labels": {"reads": [{"table": "attorney_billing_rates", "columns": ["feedtype", "application"]}, {"table": "safetytesting", "columns": ["product_type_code", "stat_id"]}], "writes": [{"table": "investors", "columns": ["product_type_code", "stat_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table course_authors_and_tutors --columns amount_payment,sd_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "course_authors_and_tutors", "columns": ["amount_payment", "sd_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.user_rating > 154).all()\n# src table: investment_rounds\nengine.execute(\"INSERT INTO investment SELECT * FROM investment_rounds\")\n", "labels": {"reads": [{"table": "investment_rounds", "columns": null}], "writes": [{"table": "investment", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mailshot_campaigns SELECT detection_id, time_second FROM ads.ads_exposure_di WHERE detection_id > 224\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_exposure_di", "columns": ["detection_id", "time_second"]}], "writes": [{"table": "mailshot_campaigns", "columns": ["detection_id", "time_second"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO production_sites SELECT manufacturer, machine_id FROM waste_generation_metrics WHERE manufacturer > 374;\nEOF\n", "labels": {"reads": [{"table": "waste_generation_metrics", "columns": ["manufacturer", "machine_id"]}], "writes": [{"table": "production_sites", "columns": ["manufacturer", "machine_id"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ethicalaibudget SELECT 1\"\necho \"job start: $(date +%F)\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nset -euo pipefail\nhive -e \"INSERT INTO countryintelligenceops SELECT eventtype, match_id, author_community, volunteer_name FROM bank_info WHERE eventtype > 423\"\n", "labels": {"reads": [{"table": "bank_info", "columns": ["eventtype", "match_id", "author_community", "volunteer_name"]}], "writes": [{"table": "countryintelligenceops", "columns": ["eventtype", "match_id", "author_community", "volunteer_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO high_risk SELECT 1\"\nmkdir -p /tmp/joblog\nset -euo pipefail\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM workercontactinfo\"\n", "labels": {"reads": [{"table": "workercontactinfo", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"convictions\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"communityengagement\")\n", "labels": {"reads": [{"table": "convictions", "columns": null}], "writes": [{"table": "communityengagement", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT sentence_id, authors FROM brandrevenue LIMIT 490\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "brandrevenue", "columns": ["sentence_id", "authors"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO fieldd_info SELECT categoryid, number_deaths, stories, device FROM production_yearly WHERE categoryid > 137\"\n", "labels": {"reads": [{"table": "production_yearly", "columns": ["categoryid", "number_deaths", "stories", "device"]}], "writes": [{"table": "fieldd_info", "columns": ["categoryid", "number_deaths", "stories", "device"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO contract_negotiations (architect_id, team_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "contract_negotiations", "columns": ["architect_id", "team_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO game_scores SELECT totaldonation, participant_name, operation_count FROM medicine WHERE totaldonation > 123\")\n", "labels": {"reads": [{"table": "medicine", "columns": ["totaldonation", "participant_name", "operation_count"]}], "writes": [{"table": "game_scores", "columns": ["totaldonation", "participant_name", "operation_count"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO airport_aircraft SELECT 1\"\nlogger.info(msg)\nimport logging\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"singer\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"product_sales\")\n", "labels": {"reads": [{"table": "singer", "columns": null}], "writes": [{"table": "product_sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO australian_states SELECT donationid, tree_type_id FROM fish_biomass WHERE donationid > 388\"\n", "labels": {"reads": [{"table": "fish_biomass", "columns": ["donationid", "tree_type_id"]}], "writes": [{"table": "australian_states", "columns": ["donationid", "tree_type_id"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO donation SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO shariah_compliant_loans SELECT risk_level, inspectionid, start_therapy FROM infrastructureprojects WHERE risk_level > 200\")\n", "labels": {"reads": [{"table": "infrastructureprojects", "columns": ["risk_level", "inspectionid", "start_therapy"]}], "writes": [{"table": "shariah_compliant_loans", "columns": ["risk_level", "inspectionid", "start_therapy"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO recycledmaterialsgarments SELECT venue, circuitid, station_id, last_name FROM humanitarian_operations WHERE venue > 375\"\n", "labels": {"reads": [{"table": "humanitarian_operations", "columns": ["venue", "circuitid", "station_id", "last_name"]}], "writes": [{"table": "recycledmaterialsgarments", "columns": ["venue", "circuitid", "station_id", "last_name"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ocean_floor SELECT * FROM legacy\ncur.execute(\"SELECT element_id, supplier_company_id FROM movie LIMIT 306\")\n", "labels": {"reads": [{"table": "movie", "columns": ["element_id", "supplier_company_id"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO sitem SELECT 1\"\ntrap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO intelligencesatellites SELECT conferencename, number_deaths, wheels FROM mappinglengths WHERE conferencename > 123\"], check=True)\n", "labels": {"reads": [{"table": "mappinglengths", "columns": ["conferencename", "number_deaths", "wheels"]}], "writes": [{"table": "intelligencesatellites", "columns": ["conferencename", "number_deaths", "wheels"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM rural_hospitals\", conn)\ndf.to_sql(\"station_company\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "rural_hospitals", "columns": null}], "writes": [{"table": "station_company", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO login_attempts SELECT a.composer, b.publish_date FROM concert_sales a JOIN pipelines_us_canada b ON a.product_details = b.product_details\"\n", "labels": {"reads": [{"table": "concert_sales", "columns": null}, {"table": "pipelines_us_canada", "columns": null}], "writes": [{"table": "login_attempts", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"artist_info\")\nsrc.write.insertInto(\"inventory\", overwrite=True)\n", "labels": {"reads": [{"table": "artist_info", "columns": null}], "writes": [{"table": "inventory", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO user_ad_interactions SELECT workshop_group_id, aid, investor_name FROM visitor_statistics WHERE workshop_group_id > 6\"\n", "labels": {"reads": [{"table": "visitor_statistics", "columns": ["workshop_group_id", "aid", "investor_name"]}], "writes": [{"table": "user_ad_interactions", "columns": ["workshop_group_id", "aid", "investor_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM threat_severity\", conn)\ndf.to_sql(\"asteroids\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "threat_severity", "columns": null}], "writes": [{"table": "asteroids", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"cosmetics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"mart.mart_shipments_hourly\")\n", "labels": {"reads": [{"table": "cosmetics", "columns": null}], "writes": [{"table": "mart.mart_shipments_hourly", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"school\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"tokyo_water_consumption\")\n", "labels": {"reads": [{"table": "school", "columns": null}], "writes": [{"table": "tokyo_water_consumption", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO vehicles SELECT awardid, donorname FROM genetic_research WHERE awardid > 239\")\n", "labels": {"reads": [{"table": "genetic_research", "columns": ["awardid", "donorname"]}], "writes": [{"table": "vehicles", "columns": ["awardid", "donorname"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO threat_intel SELECT license_type, incident_type_code FROM ads_vendors_hourly WHERE license_type > 375\")\n", "labels": {"reads": [{"table": "ads_vendors_hourly", "columns": ["license_type", "incident_type_code"]}], "writes": [{"table": "threat_intel", "columns": ["license_type", "incident_type_code"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO sustainableproduction SELECT co_owner_count, ingredient_name FROM euro_champs_track_field WHERE co_owner_count > 77\"\n", "labels": {"reads": [{"table": "euro_champs_track_field", "columns": ["co_owner_count", "ingredient_name"]}], "writes": [{"table": "sustainableproduction", "columns": ["co_owner_count", "ingredient_name"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO org_volunteer SELECT 1\"\nlogger.info(msg)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT attendee_age, nurse FROM chemical_composition LIMIT 193;\nINSERT INTO track SELECT characteristic_data_type, container_count, council_tax_id, surname FROM mart_cart_item_di WHERE characteristic_data_type > 415;\nSQL\n", "labels": {"reads": [{"table": "chemical_composition", "columns": ["attendee_age", "nurse"]}, {"table": "mart_cart_item_di", "columns": ["characteristic_data_type", "container_count", "council_tax_id", "surname"]}], "writes": [{"table": "track", "columns": ["characteristic_data_type", "container_count", "council_tax_id", "surname"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.mart_device_log_hourly\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "mart.mart_device_log_hourly", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO ads_payments_hourly SELECT a.vessel_id, b.animal_species FROM film a JOIN cyber_incidents b ON a.awards = b.awards\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "film", "columns": null}, {"table": "cyber_incidents", "columns": null}], "writes": [{"table": "ads_payments_hourly", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model conservation_projects depends on classicgame\ndbt run --models conservation_projects --vars '{\"src\":\"classicgame\"}'\n", "labels": {"reads": [{"table": "classicgame", "columns": null}], "writes": [{"table": "conservation_projects", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.labor_cost > 206).all()\n# src table: fish_purchases\nengine.execute(\"INSERT INTO patient_outcomes SELECT * FROM fish_purchases\")\n", "labels": {"reads": [{"table": "fish_purchases", "columns": null}], "writes": [{"table": "patient_outcomes", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO fault_log_parts SELECT 1\"\ntrap 'echo failed' ERR\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT market_value, temperature FROM bi.bi_risk_score_full LIMIT 340\")\nrows = cur.fetchall()\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "bi.bi_risk_score_full", "columns": ["market_value", "temperature"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO mart.mart_vendors SELECT 1\"\nlogger.info(msg)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"digital_divide_initiatives\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"stg.stg_risk_score\")\n", "labels": {"reads": [{"table": "digital_divide_initiatives", "columns": null}], "writes": [{"table": "stg.stg_risk_score", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table marketingbudget --target-dir /tmp/land\n", "labels": {"reads": [{"table": "marketingbudget", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM stg.users\", conn)\ndf.to_sql(\"students_enrollment\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "stg.users", "columns": null}], "writes": [{"table": "students_enrollment", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"fuel_consumption\")\nsink_to_output(df, \"smart_contracts\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "fuel_consumption", "columns": null}], "writes": [{"table": "smart_contracts", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.country1 > 71).all()\n# src table: communitycourts\nengine.execute(\"INSERT INTO oceania_countries SELECT * FROM communitycourts\")\n", "labels": {"reads": [{"table": "communitycourts", "columns": null}], "writes": [{"table": "oceania_countries", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"city_budgets\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"scores\")\n", "labels": {"reads": [{"table": "city_budgets", "columns": null}], "writes": [{"table": "scores", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bi_campaigns_delta depends on mentalhealthparityviolations\ndbt run -s bi_campaigns_delta --vars '{\"source_table\":\"mentalhealthparityviolations\"}'\n", "labels": {"reads": [{"table": "mentalhealthparityviolations", "columns": null}], "writes": [{"table": "bi_campaigns_delta", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT i_id, form_name FROM dw.clicks_di LIMIT 275;\nINSERT INTO innovation_projects SELECT province_id, dependent_name, case_id FROM shared_ebikes WHERE province_id > 412;\nSQL\n", "labels": {"reads": [{"table": "dw.clicks_di", "columns": ["i_id", "form_name"]}, {"table": "shared_ebikes", "columns": ["province_id", "dependent_name", "case_id"]}], "writes": [{"table": "innovation_projects", "columns": ["province_id", "dependent_name", "case_id"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO carbon_offset_programs SELECT undergraduate, first_donation_date, num_shariah_compliant_investments, budget_type_code FROM unesco_intangible_heritage WHERE undergraduate > 278\"\n", "labels": {"reads": [{"table": "unesco_intangible_heritage", "columns": ["undergraduate", "first_donation_date", "num_shariah_compliant_investments", "budget_type_code"]}], "writes": [{"table": "carbon_offset_programs", "columns": ["undergraduate", "first_donation_date", "num_shariah_compliant_investments", "budget_type_code"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_projects\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"ocean_floor\")\n", "labels": {"reads": [{"table": "rural_projects", "columns": null}], "writes": [{"table": "ocean_floor", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dwd.products_hourly SELECT form_id, is_sustainable FROM waste_data WHERE form_id > 218\"], check=True)\n", "labels": {"reads": [{"table": "waste_data", "columns": ["form_id", "is_sustainable"]}], "writes": [{"table": "dwd.products_hourly", "columns": ["form_id", "is_sustainable"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT sport, incident_type_description FROM healthcare_centers\", engine)\nmetrics.append(round(score, 4))\nimport logging\ndf.to_sql(\"dysprosiumproduction\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "healthcare_centers", "columns": ["sport", "incident_type_description"]}], "writes": [{"table": "dysprosiumproduction", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO bi.bi_risk_score_delta SELECT sqft, security_level FROM ref_document_status WHERE sqft > 244\"], check=True)\n", "labels": {"reads": [{"table": "ref_document_status", "columns": ["sqft", "security_level"]}], "writes": [{"table": "bi.bi_risk_score_delta", "columns": ["sqft", "security_level"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.hardware_colours > 119).all()\n# src table: communitypolicing\nengine.execute(\"INSERT INTO region SELECT * FROM communitypolicing\")\n", "labels": {"reads": [{"table": "communitypolicing", "columns": null}], "writes": [{"table": "region", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nsql = \"INSERT INTO dw.shipments_di SELECT a.address_content, b.ocean_name FROM tracklists a JOIN cultivators b ON a.date = b.date\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tracklists", "columns": null}, {"table": "cultivators", "columns": null}], "writes": [{"table": "dw.shipments_di", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT hospital_id, rental_date FROM manager_award LIMIT 283\")\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO geneva_motor_show SELECT ticket_id, disaster_id, ei_category, comments FROM song WHERE ticket_id > 68\")\n", "labels": {"reads": [{"table": "manager_award", "columns": ["hospital_id", "rental_date"]}, {"table": "song", "columns": ["ticket_id", "disaster_id", "ei_category", "comments"]}], "writes": [{"table": "geneva_motor_show", "columns": ["ticket_id", "disaster_id", "ei_category", "comments"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nsql = \"INSERT INTO eventparticipation SELECT a.num_of_factories, b.acidity_level FROM auctions a JOIN crime_reports b ON a.gender_diversity = b.gender_diversity\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "auctions", "columns": null}, {"table": "crime_reports", "columns": null}], "writes": [{"table": "eventparticipation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM talent_acquisition\", conn)\ndf.to_sql(\"sustainable_projects\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "talent_acquisition", "columns": null}], "writes": [{"table": "sustainable_projects", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model project depends on dwd.dwd_orders_daily\ndbt build -s project --vars 'source: dwd.dwd_orders_daily'\n", "labels": {"reads": [{"table": "dwd.dwd_orders_daily", "columns": null}], "writes": [{"table": "project", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO inspections SELECT 1\"\nlogger.info(msg)\nimport logging\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM safetyincidents\", conn)\ndf.to_sql(\"ods.ods_risk_score_df\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "safetyincidents", "columns": null}], "writes": [{"table": "ods.ods_risk_score_df", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM regions\"\n", "labels": {"reads": [{"table": "regions", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO manufacturing_processes SELECT date_became_customer, date_of_ceremony FROM appliances WHERE date_became_customer > 455\")\n", "labels": {"reads": [{"table": "appliances", "columns": ["date_became_customer", "date_of_ceremony"]}], "writes": [{"table": "manufacturing_processes", "columns": ["date_became_customer", "date_of_ceremony"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT task_details, attendance FROM performances LIMIT 371\")\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO management SELECT co2_reduction, startdate FROM disease_prevalence WHERE co2_reduction > 471\")\n", "labels": {"reads": [{"table": "performances", "columns": ["task_details", "attendance"]}, {"table": "disease_prevalence", "columns": ["co2_reduction", "startdate"]}], "writes": [{"table": "management", "columns": ["co2_reduction", "startdate"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nexport TZ=Asia/Shanghai\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO device SELECT participant_type_code, post_date, years_working FROM recycling_stats WHERE participant_type_code > 394\"\n", "labels": {"reads": [{"table": "recycling_stats", "columns": ["participant_type_code", "post_date", "years_working"]}], "writes": [{"table": "device", "columns": ["participant_type_code", "post_date", "years_working"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO crypto_transactions SELECT 1\"\nlogger.info(msg)\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.game_count > 292).all()\n# src table: mart.coupon_use_hourly\nengine.execute(\"INSERT INTO labor_cost SELECT * FROM mart.coupon_use_hourly\")\n", "labels": {"reads": [{"table": "mart.coupon_use_hourly", "columns": null}], "writes": [{"table": "labor_cost", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM supply_chain\", conn)\ndf.to_sql(\"organizations\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "supply_chain", "columns": null}], "writes": [{"table": "organizations", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"developers\").toPandas()\ndf[[\"isfirstattendee\", \"gamesplayed\"]].to_sql(\"precipitation_data\", engine, index=False)\n", "labels": {"reads": [{"table": "developers", "columns": null}], "writes": [{"table": "precipitation_data", "columns": ["isfirstattendee", "gamesplayed"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods.sessions_daily\")\nsrc.write.insertInto(\"textile_suppliers\", overwrite=True)\n", "labels": {"reads": [{"table": "ods.sessions_daily", "columns": null}], "writes": [{"table": "textile_suppliers", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO race SELECT co2_reduction, impact_score, activity_date, movement FROM bi.events_delta WHERE co2_reduction > 60\"\n", "labels": {"reads": [{"table": "bi.events_delta", "columns": ["co2_reduction", "impact_score", "activity_date", "movement"]}], "writes": [{"table": "race", "columns": ["co2_reduction", "impact_score", "activity_date", "movement"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"pilot\")\nsrc.write.insertInto(\"ocean_species\", overwrite=True)\n", "labels": {"reads": [{"table": "pilot", "columns": null}], "writes": [{"table": "ocean_species", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model commercialbuildings depends on creativeais\ndbt run --models commercialbuildings --vars '{\"src\":\"creativeais\"}'\n", "labels": {"reads": [{"table": "creativeais", "columns": null}], "writes": [{"table": "commercialbuildings", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO org_donation SELECT a.fund_name, b.part_id FROM dwd.dwd_coupon_use_df a JOIN ocean_species b ON a.friend = b.friend\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "dwd.dwd_coupon_use_df", "columns": null}, {"table": "ocean_species", "columns": null}], "writes": [{"table": "org_donation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO clothingitems SELECT focal_length_mm, athlete_id FROM stg.stg_users_di WHERE focal_length_mm > 390\"\n", "labels": {"reads": [{"table": "stg.stg_users_di", "columns": ["focal_length_mm", "athlete_id"]}], "writes": [{"table": "clothingitems", "columns": ["focal_length_mm", "athlete_id"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO bus_fare_collection (observation_date, invoicedate) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "bus_fare_collection", "columns": ["observation_date", "invoicedate"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO trainingprograms SELECT a.sportname, b.rating_id FROM disabilityadvocacy a JOIN threat_intel b ON a.license_type = b.license_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "disabilityadvocacy", "columns": null}, {"table": "threat_intel", "columns": null}], "writes": [{"table": "trainingprograms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model medals depends on beverages\ndbt run --select medals --vars 'source: beverages'\n", "labels": {"reads": [{"table": "beverages", "columns": null}], "writes": [{"table": "medals", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO completed_training SELECT a.experiment_name, b.reported_date FROM cargo_equipment a JOIN documents b ON a.workout_duration = b.workout_duration\"\n", "labels": {"reads": [{"table": "cargo_equipment", "columns": null}, {"table": "documents", "columns": null}], "writes": [{"table": "completed_training", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.coupon_use_daily\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"electoral_register\")\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": null}], "writes": [{"table": "electoral_register", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ocean_floor_mapping SELECT game_id, audienceid FROM stg.coupon_use WHERE game_id > 205\"\n", "labels": {"reads": [{"table": "stg.coupon_use", "columns": ["game_id", "audienceid"]}], "writes": [{"table": "ocean_floor_mapping", "columns": ["game_id", "audienceid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table cargo_data --columns habitat_name,ai_algorithm_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "cargo_data", "columns": ["habitat_name", "ai_algorithm_id"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT frameworkcountry, courses FROM passenger_trips LIMIT 223\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "passenger_trips", "columns": ["frameworkcountry", "courses"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT overall_rating, annual_entry_exit FROM ads.ads_exposure_daily\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"virtual_tour_stats\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "ads.ads_exposure_daily", "columns": ["overall_rating", "annual_entry_exit"]}], "writes": [{"table": "virtual_tour_stats", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model projecttimelinebybudget depends on salesperson\ndbt run --models projecttimelinebybudget --vars '{\"source_table\":\"salesperson\"}'\n", "labels": {"reads": [{"table": "salesperson", "columns": null}], "writes": [{"table": "projecttimelinebybudget", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ship_agent SELECT ad_id, milliseconds FROM mineral_extraction WHERE ad_id > 452\"], check=True)\n", "labels": {"reads": [{"table": "mineral_extraction", "columns": ["ad_id", "milliseconds"]}], "writes": [{"table": "ship_agent", "columns": ["ad_id", "milliseconds"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO completed_training (injury_count, scan_date) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "completed_training", "columns": ["injury_count", "scan_date"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"carbon_offset_south_america\")\nwrite_to_target(df, \"coral_reefs\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": null}], "writes": [{"table": "coral_reefs", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO spending (characteristic_id, mid) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "spending", "columns": ["characteristic_id", "mid"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"stg.stg_clicks_delta\").toPandas()\ndf[[\"dishname\", \"day_of_week\"]].to_sql(\"public.collected_fare\", engine, index=False)\n", "labels": {"reads": [{"table": "stg.stg_clicks_delta", "columns": null}], "writes": [{"table": "public.collected_fare", "columns": ["dishname", "day_of_week"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\nexport TZ=Asia/Shanghai\nsqoop import --connect \"$JDBC\" --table mart.mart_coupon_use_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "mart.mart_coupon_use_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"carbon_offset_south_america\")\nsrc.write.insertInto(\"bioprocesses\", overwrite=True)\n", "labels": {"reads": [{"table": "carbon_offset_south_america", "columns": null}], "writes": [{"table": "bioprocesses", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO manufacturingplants SELECT analysis_date, neighborhood FROM socially_responsible_loans WHERE analysis_date > 355\"\n", "labels": {"reads": [{"table": "socially_responsible_loans", "columns": ["analysis_date", "neighborhood"]}], "writes": [{"table": "manufacturingplants", "columns": ["analysis_date", "neighborhood"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO sustainableprojects SELECT session_language, founders_lgbtq, organized_by FROM apac_hotel_views WHERE session_language > 251;\nEOF\n", "labels": {"reads": [{"table": "apac_hotel_views", "columns": ["session_language", "founders_lgbtq", "organized_by"]}], "writes": [{"table": "sustainableprojects", "columns": ["session_language", "founders_lgbtq", "organized_by"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO forests SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO humanitarian_aid (creation_date, journalist_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "humanitarian_aid", "columns": ["creation_date", "journalist_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO results SELECT a.animal, b.attribute_value FROM employeepromotions a JOIN playergamehistory b ON a.origin = b.origin\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "employeepromotions", "columns": null}, {"table": "playergamehistory", "columns": null}], "writes": [{"table": "results", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO agriculturalinvestments SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mental_health_clinics\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"threatintelligence\")\n", "labels": {"reads": [{"table": "mental_health_clinics", "columns": null}], "writes": [{"table": "threatintelligence", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT cost_id, day_of_week FROM genderdistribution LIMIT 385\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "genderdistribution", "columns": ["cost_id", "day_of_week"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nresult = value * ratio + offset\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO branch SELECT shop_details, artist_id, dno, friend FROM concert_sales WHERE shop_details > 308\"], check=True)\n", "labels": {"reads": [{"table": "concert_sales", "columns": ["shop_details", "artist_id", "dno", "friend"]}], "writes": [{"table": "branch", "columns": ["shop_details", "artist_id", "dno", "friend"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM criminalcases\"\n", "labels": {"reads": [{"table": "criminalcases", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dws_cart_item (archaeologist_id, college_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dws_cart_item", "columns": ["archaeologist_id", "college_id"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT recorded_by_staff_id, mouse_id FROM dws.dws_orders_full\", engine)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"reservations\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "dws.dws_orders_full", "columns": ["recorded_by_staff_id", "mouse_id"]}], "writes": [{"table": "reservations", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dws.dws_shipments_full SELECT firstdonationdate, name_first FROM artists WHERE firstdonationdate > 28\"], check=True)\n", "labels": {"reads": [{"table": "artists", "columns": ["firstdonationdate", "name_first"]}], "writes": [{"table": "dws.dws_shipments_full", "columns": ["firstdonationdate", "name_first"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"global_sales_2022\")\nsrc.write.insertInto(\"network_infrastructure\", overwrite=True)\n", "labels": {"reads": [{"table": "global_sales_2022", "columns": null}], "writes": [{"table": "network_infrastructure", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"iron_ore_production\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "iron_ore_production", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO iron_ore_production SELECT role_name, personal_name, habitat_name, played FROM training_programs WHERE role_name > 318;\nEOF\n", "labels": {"reads": [{"table": "training_programs", "columns": ["role_name", "personal_name", "habitat_name", "played"]}], "writes": [{"table": "iron_ore_production", "columns": ["role_name", "personal_name", "habitat_name", "played"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO rare_earth_companies (staff_gender, garment) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "rare_earth_companies", "columns": ["staff_gender", "garment"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO maintenance_contracts SELECT num_songs, end_date, export_country FROM fairness_scores WHERE num_songs > 326;\nEOF\n", "labels": {"reads": [{"table": "fairness_scores", "columns": ["num_songs", "end_date", "export_country"]}], "writes": [{"table": "maintenance_contracts", "columns": ["num_songs", "end_date", "export_country"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table biosensors.projects --target-dir /tmp/land\n", "labels": {"reads": [{"table": "biosensors.projects", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO issues SELECT budget_amount, launch_year, system, account_details FROM dws_products WHERE budget_amount > 479\"\n", "labels": {"reads": [{"table": "dws_products", "columns": ["budget_amount", "launch_year", "system", "account_details"]}], "writes": [{"table": "issues", "columns": ["budget_amount", "launch_year", "system", "account_details"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.institution_id > 108).all()\n# src table: tech_accessibility_funding\nengine.execute(\"INSERT INTO labor_productivity SELECT * FROM tech_accessibility_funding\")\n", "labels": {"reads": [{"table": "tech_accessibility_funding", "columns": null}], "writes": [{"table": "labor_productivity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tvshows\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "tvshows", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO jp_schema.policy_areas (detention_type_code, num_of_factories) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "jp_schema.policy_areas", "columns": ["detention_type_code", "num_of_factories"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"rural_development.agriculture_projects\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"smart_contracts_table\")\n", "labels": {"reads": [{"table": "rural_development.agriculture_projects", "columns": null}], "writes": [{"table": "smart_contracts_table", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nset -euo pipefail\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO provinces SELECT altitude, delivery_status, school_code, initiative_id FROM ods.member_point_df WHERE altitude > 339\"\n", "labels": {"reads": [{"table": "ods.member_point_df", "columns": ["altitude", "delivery_status", "school_code", "initiative_id"]}], "writes": [{"table": "provinces", "columns": ["altitude", "delivery_status", "school_code", "initiative_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO stg.stg_users_full SELECT partner_id, open_date, movie FROM state_water_usage WHERE partner_id > 332\")\n", "labels": {"reads": [{"table": "state_water_usage", "columns": ["partner_id", "open_date", "movie"]}], "writes": [{"table": "stg.stg_users_full", "columns": ["partner_id", "open_date", "movie"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_dataset(ctx, \"brandrevenue\")\npersist_to_store(df, \"defense_diplomacy\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "brandrevenue", "columns": null}], "writes": [{"table": "defense_diplomacy", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO laptimes SELECT 1\"\nlogger.info(msg)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mart.shipments_delta\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"view_product_availability\")\n", "labels": {"reads": [{"table": "mart.shipments_delta", "columns": null}], "writes": [{"table": "view_product_availability", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = load_input(ctx, \"mars_rovers\")\nsink_to_output(df, \"cosmetics.lipstick_spf_data\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "mars_rovers", "columns": null}], "writes": [{"table": "cosmetics.lipstick_spf_data", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"innovation_grants\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ai_safety_incidents\")\n", "labels": {"reads": [{"table": "innovation_grants", "columns": null}], "writes": [{"table": "ai_safety_incidents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = get_source(ctx, \"bookings\")\nupsert_to_sink(df, \"seafood\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "bookings", "columns": null}], "writes": [{"table": "seafood", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO spacecrafts SELECT playtime, movement FROM rent_arrears WHERE playtime > 47\")\n", "labels": {"reads": [{"table": "rent_arrears", "columns": ["playtime", "movement"]}], "writes": [{"table": "spacecrafts", "columns": ["playtime", "movement"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table mining_operation_data --columns end_speed,gender_group --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "mining_operation_data", "columns": ["end_speed", "gender_group"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.coupon_use_daily\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dwd.coupon_use_daily", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dwd_sessions_df SELECT 1\"\nlogger.info(msg)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-logged-not-executed", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model bike_stations depends on ods.products_hourly\ndbt build --models bike_stations --vars '{\"src\":\"ods.products_hourly\"}'\n", "labels": {"reads": [{"table": "ods.products_hourly", "columns": null}], "writes": [{"table": "bike_stations", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"user_activity\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"sales\")\n", "labels": {"reads": [{"table": "user_activity", "columns": null}], "writes": [{"table": "sales", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM researchprojects\", conn)\ndf.to_sql(\"workouts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "researchprojects", "columns": null}], "writes": [{"table": "workouts", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO safetytests SELECT rebounds, enrollment_date, date_to, playdate FROM deliveryaddresses WHERE rebounds > 362\"], check=True)\n", "labels": {"reads": [{"table": "deliveryaddresses", "columns": ["rebounds", "enrollment_date", "date_to", "playdate"]}], "writes": [{"table": "safetytests", "columns": ["rebounds", "enrollment_date", "date_to", "playdate"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT problem_id, project_name FROM exit_strategy LIMIT 339;\nINSERT INTO workersalaries SELECT province, vehicleid, used_kb, suburb FROM video_games WHERE province > 294;\nSQL\n", "labels": {"reads": [{"table": "exit_strategy", "columns": ["problem_id", "project_name"]}, {"table": "video_games", "columns": ["province", "vehicleid", "used_kb", "suburb"]}], "writes": [{"table": "workersalaries", "columns": ["province", "vehicleid", "used_kb", "suburb"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO us_cities SELECT a.showid, b.eia_date FROM renewabletypes a JOIN teacher_professional_development b ON a.attorney_id = b.attorney_id\"\n", "labels": {"reads": [{"table": "renewabletypes", "columns": null}, {"table": "teacher_professional_development", "columns": null}], "writes": [{"table": "us_cities", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT facility_code, state_county FROM marketing_budgets LIMIT 203\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "marketing_budgets", "columns": ["facility_code", "state_county"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"product_info\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"technology_access\")\n", "labels": {"reads": [{"table": "product_info", "columns": null}], "writes": [{"table": "technology_access", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO plays_games SELECT alid, farmid, endtime FROM police_emergencies WHERE alid > 412;\nEOF\n", "labels": {"reads": [{"table": "police_emergencies", "columns": ["alid", "farmid", "endtime"]}], "writes": [{"table": "plays_games", "columns": ["alid", "farmid", "endtime"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table dwd.inventory_df --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dwd.inventory_df", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bikerental (saleamount, transaction_amount) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bikerental", "columns": ["saleamount", "transaction_amount"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\ntotal = sum(x ** 2 for x in range(100))\nprint(round(total / 7, 3))\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "py-pure-compute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.ads_products_full SELECT license_id, facultyid, artist_name FROM workplace_safety WHERE license_id > 24\"], check=True)\n", "labels": {"reads": [{"table": "workplace_safety", "columns": ["license_id", "facultyid", "artist_name"]}], "writes": [{"table": "ads.ads_products_full", "columns": ["license_id", "facultyid", "artist_name"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table city_properties --columns crop_name,meal_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "city_properties", "columns": ["crop_name", "meal_date"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT event_type_id, menu_id FROM domesticconferences LIMIT 307\")\nresult = value * ratio + offset\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO ods.products_hourly SELECT accreditation_level, society FROM dws.dws_inventory_hourly WHERE accreditation_level > 148\")\n", "labels": {"reads": [{"table": "domesticconferences", "columns": ["event_type_id", "menu_id"]}, {"table": "dws.dws_inventory_hourly", "columns": ["accreditation_level", "society"]}], "writes": [{"table": "ods.products_hourly", "columns": ["accreditation_level", "society"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "set -euo pipefail\necho \"job start: $(date +%F)\"\nmkdir -p /tmp/joblog\nhive -e \"INSERT INTO dwd.exposure_hourly SELECT vaccine_type, pilot_name, system_id FROM threats WHERE vaccine_type > 45\"\n", "labels": {"reads": [{"table": "threats", "columns": ["vaccine_type", "pilot_name", "system_id"]}], "writes": [{"table": "dwd.exposure_hourly", "columns": ["vaccine_type", "pilot_name", "system_id"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO ads.ads_events_df SELECT virtual_tour_sessions, has_access FROM impact_investments WHERE virtual_tour_sessions > 300\"], check=True)\n", "labels": {"reads": [{"table": "impact_investments", "columns": ["virtual_tour_sessions", "has_access"]}], "writes": [{"table": "ads.ads_events_df", "columns": ["virtual_tour_sessions", "has_access"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO pipelines SELECT * FROM legacy\ncur.execute(\"SELECT iata, event_count FROM department_publications LIMIT 239\")\n", "labels": {"reads": [{"table": "department_publications", "columns": ["iata", "event_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO basketball_teams SELECT days_held, submission_id, meter_300, amount_donated FROM workout WHERE days_held > 323\"], check=True)\n", "labels": {"reads": [{"table": "workout", "columns": ["days_held", "submission_id", "meter_300", "amount_donated"]}], "writes": [{"table": "basketball_teams", "columns": ["days_held", "submission_id", "meter_300", "amount_donated"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO innovation_metrics (visit_year, metric_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "innovation_metrics", "columns": ["visit_year", "metric_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO tryout SELECT farmer_name, attraction_type_code, carriername FROM restaurants WHERE farmer_name > 437;\nEOF\n", "labels": {"reads": [{"table": "restaurants", "columns": ["farmer_name", "attraction_type_code", "carriername"]}], "writes": [{"table": "tryout", "columns": ["farmer_name", "attraction_type_code", "carriername"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO bi.bi_campaigns_daily (event_name, practice_id) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "bi.bi_campaigns_daily", "columns": ["event_name", "practice_id"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO shipments SELECT * FROM legacy\ncur.execute(\"SELECT asset_id, clientid FROM bi.bi_inventory_full LIMIT 48\")\n", "labels": {"reads": [{"table": "bi.bi_inventory_full", "columns": ["asset_id", "clientid"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT trainingid, grant_start_date FROM spacecraft_manufacturers LIMIT 233\")\nimport logging\nspark.sql(\"INSERT INTO organisations SELECT cause_name, stayid FROM fish_feed_factories WHERE cause_name > 341\")\n", "labels": {"reads": [{"table": "spacecraft_manufacturers", "columns": ["trainingid", "grant_start_date"]}, {"table": "fish_feed_factories", "columns": ["cause_name", "stayid"]}], "writes": [{"table": "organisations", "columns": ["cause_name", "stayid"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO airlines (account_details, total_value_purchased) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "airlines", "columns": ["account_details", "total_value_purchased"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.issue_month > 285).all()\n# src table: electric_taxis\nengine.execute(\"INSERT INTO labor_productivity SELECT * FROM electric_taxis\")\n", "labels": {"reads": [{"table": "electric_taxis", "columns": null}], "writes": [{"table": "labor_productivity", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO producersnewmexico SELECT is_vegan, reason, brand FROM ship_agent WHERE is_vegan > 112\"\n", "labels": {"reads": [{"table": "ship_agent", "columns": ["is_vegan", "reason", "brand"]}], "writes": [{"table": "producersnewmexico", "columns": ["is_vegan", "reason", "brand"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads_coupon_use_full\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"support\")\n", "labels": {"reads": [{"table": "ads_coupon_use_full", "columns": null}], "writes": [{"table": "support", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nimport logging\nspark.sql(\"INSERT INTO spacecraftmanufacturing SELECT airport_id, countid, lot_id, num_beds FROM workshops WHERE airport_id > 275\")\n", "labels": {"reads": [{"table": "workshops", "columns": ["airport_id", "countid", "lot_id", "num_beds"]}], "writes": [{"table": "spacecraftmanufacturing", "columns": ["airport_id", "countid", "lot_id", "num_beds"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM climate_finance_asia\"\n", "labels": {"reads": [{"table": "climate_finance_asia", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT dispensaryid, num_fans FROM news_stories\", engine)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\ndf.to_sql(\"faculty_participates_in\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "news_stories", "columns": ["dispensaryid", "num_fans"]}], "writes": [{"table": "faculty_participates_in", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT killed, milestone FROM arrivals\", engine)\nresult = value * ratio + offset\ndf.to_sql(\"public.collected_fare\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "arrivals", "columns": ["killed", "milestone"]}], "writes": [{"table": "public.collected_fare", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO coralreefs SELECT a.retailer_name, b.county FROM sales_2 a JOIN infrastructure b ON a.orderdate = b.orderdate\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "sales_2", "columns": null}, {"table": "infrastructure", "columns": null}], "writes": [{"table": "coralreefs", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO visualartprograms SELECT * FROM legacy\ncur.execute(\"SELECT review_score, section_title FROM dancefunding LIMIT 30\")\n", "labels": {"reads": [{"table": "dancefunding", "columns": ["review_score", "section_title"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = pull_dataset(ctx, \"legislation\")\npush_to_sink(df, \"communitycenters\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "legislation", "columns": null}], "writes": [{"table": "communitycenters", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO equipment_maintenance SELECT vehicleid, purchase_details, resource_name FROM bike_station_info WHERE vehicleid > 16\"\n", "labels": {"reads": [{"table": "bike_station_info", "columns": ["vehicleid", "purchase_details", "resource_name"]}], "writes": [{"table": "equipment_maintenance", "columns": ["vehicleid", "purchase_details", "resource_name"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "# model union_membership depends on trees\ndbt run --select union_membership --vars '{\"src\":\"trees\"}'\n", "labels": {"reads": [{"table": "trees", "columns": null}], "writes": [{"table": "union_membership", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"mentalhealthparityscores\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"agricultural_innovations\")\n", "labels": {"reads": [{"table": "mentalhealthparityscores", "columns": null}], "writes": [{"table": "agricultural_innovations", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO organization_contact_individuals SELECT * FROM legacy\ncur.execute(\"SELECT union_members, injury_count FROM movie_ratings LIMIT 83\")\n", "labels": {"reads": [{"table": "movie_ratings", "columns": ["union_members", "injury_count"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO shrimp_farms (reported, servicename) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "shrimp_farms", "columns": ["reported", "servicename"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nresult = value * ratio + offset\nsql = \"INSERT INTO seamounts SELECT a.is_commercial, b.oil_volume FROM inventory a JOIN producers b ON a.permit_id = b.permit_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "inventory", "columns": null}, {"table": "producers", "columns": null}], "writes": [{"table": "seamounts", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"rural_clinics\")\nsrc.write.insertInto(\"open_pedagogy_courses\", overwrite=True)\n", "labels": {"reads": [{"table": "rural_clinics", "columns": null}], "writes": [{"table": "open_pedagogy_courses", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT treatment_type, winery FROM justice_schemas.legal_tech_providers LIMIT 303;\nINSERT INTO multimodal_trips SELECT installation_date, daily_consumption, art_id, player_name FROM africa_schema.african_mines WHERE installation_date > 401;\nSQL\n", "labels": {"reads": [{"table": "justice_schemas.legal_tech_providers", "columns": ["treatment_type", "winery"]}, {"table": "africa_schema.african_mines", "columns": ["installation_date", "daily_consumption", "art_id", "player_name"]}], "writes": [{"table": "multimodal_trips", "columns": ["installation_date", "daily_consumption", "art_id", "player_name"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT therapy_type, volunteerid FROM teacher_development_race LIMIT 477\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "teacher_development_race", "columns": ["therapy_type", "volunteerid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"tv_shows_genre\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"dw.dw_payments_full\")\n", "labels": {"reads": [{"table": "tv_shows_genre", "columns": null}], "writes": [{"table": "dw.dw_payments_full", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO ads.ads_device_log_di SELECT apt_number, name_first, energy_generated FROM manufacturers WHERE apt_number > 336\"\n", "labels": {"reads": [{"table": "manufacturers", "columns": ["apt_number", "name_first", "energy_generated"]}], "writes": [{"table": "ads.ads_device_log_di", "columns": ["apt_number", "name_first", "energy_generated"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO labor_statistics SELECT news_story_id, pallet_id, building, document_date FROM public.police_calls WHERE news_story_id > 23\"], check=True)\n", "labels": {"reads": [{"table": "public.police_calls", "columns": ["news_story_id", "pallet_id", "building", "document_date"]}], "writes": [{"table": "labor_statistics", "columns": ["news_story_id", "pallet_id", "building", "document_date"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO marketingbudget SELECT product_type, mailing_date, staff_last_name FROM tree_habitat_associations WHERE product_type > 344;\nEOF\n", "labels": {"reads": [{"table": "tree_habitat_associations", "columns": ["product_type", "mailing_date", "staff_last_name"]}], "writes": [{"table": "marketingbudget", "columns": ["product_type", "mailing_date", "staff_last_name"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO causes_insert_2 SELECT mineid, station_name FROM mine_workforce WHERE mineid > 201\")\n", "labels": {"reads": [{"table": "mine_workforce", "columns": ["mineid", "station_name"]}], "writes": [{"table": "causes_insert_2", "columns": ["mineid", "station_name"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO marine_species_arctic_ocean SELECT project_education, speed, date_joined_staff FROM rural_feeder_roads WHERE project_education > 243\"], check=True)\n", "labels": {"reads": [{"table": "rural_feeder_roads", "columns": ["project_education", "speed", "date_joined_staff"]}], "writes": [{"table": "marine_species_arctic_ocean", "columns": ["project_education", "speed", "date_joined_staff"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"emerging_markets.digital_assets\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "emerging_markets.digital_assets", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO songs SELECT a.observation_id, b.mediatorid FROM drug_sales a JOIN playergamehistory b ON a.adoption_date = b.adoption_date\"\n", "labels": {"reads": [{"table": "drug_sales", "columns": null}, {"table": "playergamehistory", "columns": null}], "writes": [{"table": "songs", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"route\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "route", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT round, org_name FROM dailystreams LIMIT 193\")\nimport logging\nspark.sql(\"INSERT INTO landfill_capacity_city_v2 SELECT complaint_status_code, aid, screening, vaccine_name FROM patienttreatments WHERE complaint_status_code > 332\")\n", "labels": {"reads": [{"table": "dailystreams", "columns": ["round", "org_name"]}, {"table": "patienttreatments", "columns": ["complaint_status_code", "aid", "screening", "vaccine_name"]}], "writes": [{"table": "landfill_capacity_city_v2", "columns": ["complaint_status_code", "aid", "screening", "vaccine_name"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO hair_care_sales SELECT * FROM legacy\ncur.execute(\"SELECT dateundergoes, fan_age FROM music LIMIT 250\")\n", "labels": {"reads": [{"table": "music", "columns": ["dateundergoes", "fan_age"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT threats, brand_mentioned FROM review LIMIT 17\")\nrows = cur.fetchall()\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "review", "columns": ["threats", "brand_mentioned"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.customer_first_name > 487).all()\n# src table: dorm\nengine.execute(\"INSERT INTO maintenance SELECT * FROM dorm\")\n", "labels": {"reads": [{"table": "dorm", "columns": null}], "writes": [{"table": "maintenance", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM garmentproduction\"\n", "labels": {"reads": [{"table": "garmentproduction", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO music SELECT share_in_percent, capital FROM strandings WHERE share_in_percent > 273\"], check=True)\n", "labels": {"reads": [{"table": "strandings", "columns": ["share_in_percent", "capital"]}], "writes": [{"table": "music", "columns": ["share_in_percent", "capital"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO volunteerprograms SELECT founder_identity, trip_id, mission_name, count FROM defense_projects WHERE founder_identity > 425\"], check=True)\n", "labels": {"reads": [{"table": "defense_projects", "columns": ["founder_identity", "trip_id", "mission_name", "count"]}], "writes": [{"table": "volunteerprograms", "columns": ["founder_identity", "trip_id", "mission_name", "count"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT negative, building_description FROM fishcaught LIMIT 141\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "fishcaught", "columns": ["negative", "building_description"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "train"}}