{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"public_buses\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "public_buses", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "if not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO crane SELECT pilot_name, assets_billion, date_closed FROM hospital_beds WHERE pilot_name > 467\")\n", "labels": {"reads": [{"table": "hospital_beds", "columns": ["pilot_name", "assets_billion", "date_closed"]}], "writes": [{"table": "crane", "columns": ["pilot_name", "assets_billion", "date_closed"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO countryincome SELECT org_name, institution_id, dish FROM dws_refunds_daily WHERE org_name > 8\"))\n", "labels": {"reads": [{"table": "dws_refunds_daily", "columns": ["org_name", "institution_id", "dish"]}], "writes": [{"table": "countryincome", "columns": ["org_name", "institution_id", "dish"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO journalist SELECT discovered_date, location_code, rebounds, host_country FROM resourcemanagement WHERE discovered_date > 43\")\n", "labels": {"reads": [{"table": "resourcemanagement", "columns": ["discovered_date", "location_code", "rebounds", "host_country"]}], "writes": [{"table": "journalist", "columns": ["discovered_date", "location_code", "rebounds", "host_country"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO mart.sessions_hourly SELECT 1\"\nRETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\n", "labels": {"reads": [], "writes": []}, "meta": {"template_id": "sh-echo-only", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO ships SELECT scoreid, part_fault_id, velocity FROM debate_people WHERE scoreid > 344\"))\n", "labels": {"reads": [{"table": "debate_people", "columns": ["scoreid", "part_fault_id", "velocity"]}], "writes": [{"table": "ships", "columns": ["scoreid", "part_fault_id", "velocity"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO affected_region SELECT astronautid, num_libraries, case_type FROM bi_exposure_daily WHERE astronautid > 34\"\n", "labels": {"reads": [{"table": "bi_exposure_daily", "columns": ["astronautid", "num_libraries", "case_type"]}], "writes": [{"table": "affected_region", "columns": ["astronautid", "num_libraries", "case_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": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"union_membership_statistics\", writes=\"readers\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "union_membership_statistics", "columns": null}], "writes": [{"table": "readers", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO tour_types 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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO csu_fees SELECT plantid, school_colors FROM images WHERE plantid > 10\"\n", "labels": {"reads": [{"table": "images", "columns": ["plantid", "school_colors"]}], "writes": [{"table": "csu_fees", "columns": ["plantid", "school_colors"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"news_articles\").where(\"dt = current_date()\")\nevents.writeTo(\"claims_processing\").append()\n", "labels": {"reads": [{"table": "news_articles", "columns": null}], "writes": [{"table": "claims_processing", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"stg_sessions_daily\", writes=\"donationdates\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "stg_sessions_daily", "columns": null}], "writes": [{"table": "donationdates", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"suppliers\").where(\"dt = current_date()\")\nevents.writeTo(\"europe_org\").append()\n", "labels": {"reads": [{"table": "suppliers", "columns": null}], "writes": [{"table": "europe_org", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT fanid, shares FROM marine_protected_areas LIMIT 91\")\nrows = cur.fetchall()\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "marine_protected_areas", "columns": ["fanid", "shares"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO products_for_hire SELECT built, case_status, uses_vr FROM techniques WHERE built > 267\"\n", "labels": {"reads": [{"table": "techniques", "columns": ["built", "case_status", "uses_vr"]}], "writes": [{"table": "products_for_hire", "columns": ["built", "case_status", "uses_vr"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"skincare_ingredients\", limit=5000)\nresult = value * ratio + offset\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "skincare_ingredients", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO startup SELECT area_id, therapy_date, portfolio_id, attraction_type_code FROM unique_devices WHERE area_id > 344\"))\n", "labels": {"reads": [{"table": "unique_devices", "columns": ["area_id", "therapy_date", "portfolio_id", "attraction_type_code"]}], "writes": [{"table": "startup", "columns": ["area_id", "therapy_date", "portfolio_id", "attraction_type_code"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"visitor_stats\").where(\"dt = current_date()\")\nevents.writeTo(\"students_in_detention\").append()\n", "labels": {"reads": [{"table": "visitor_stats", "columns": null}], "writes": [{"table": "students_in_detention", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO ferry_routes SELECT product_color, audienceid, supporter, num_stops FROM cultural_sites WHERE product_color > 465\"\n", "labels": {"reads": [{"table": "cultural_sites", "columns": ["product_color", "audienceid", "supporter", "num_stops"]}], "writes": [{"table": "ferry_routes", "columns": ["product_color", "audienceid", "supporter", "num_stops"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO dissolved_oxygen_readings SELECT pollution_id, start_year, replacement_cost FROM african_region_table WHERE pollution_id > 456\"\n", "labels": {"reads": [{"table": "african_region_table", "columns": ["pollution_id", "start_year", "replacement_cost"]}], "writes": [{"table": "dissolved_oxygen_readings", "columns": ["pollution_id", "start_year", "replacement_cost"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO unique_donors SELECT population, primaryaffiliation FROM member_workouts WHERE population > 204\"\n", "labels": {"reads": [{"table": "member_workouts", "columns": ["population", "primaryaffiliation"]}], "writes": [{"table": "unique_donors", "columns": ["population", "primaryaffiliation"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO stg.stg_products_hourly SELECT vendor_id, sale_amount, branch FROM community_education WHERE vendor_id > 328\"\n", "labels": {"reads": [{"table": "community_education", "columns": ["vendor_id", "sale_amount", "branch"]}], "writes": [{"table": "stg.stg_products_hourly", "columns": ["vendor_id", "sale_amount", "branch"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT date_and_date, classroom FROM intangible_heritage LIMIT 492\")\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO injuries SELECT max_dissolved_oxygen, deliveryid, name_last FROM classes WHERE max_dissolved_oxygen > 447\")\n", "labels": {"reads": [{"table": "intangible_heritage", "columns": ["date_and_date", "classroom"]}, {"table": "classes", "columns": ["max_dissolved_oxygen", "deliveryid", "name_last"]}], "writes": [{"table": "injuries", "columns": ["max_dissolved_oxygen", "deliveryid", "name_last"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"dwd.device_log_daily\", writes=\"dental_clinics\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dwd.device_log_daily", "columns": null}], "writes": [{"table": "dental_clinics", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO satellite_launches (supporter, date_account_opened) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "satellite_launches", "columns": ["supporter", "date_account_opened"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO smartcityinitiatives SELECT cropid, race FROM ocean_temperature WHERE cropid > 282\"\n", "labels": {"reads": [{"table": "ocean_temperature", "columns": ["cropid", "race"]}], "writes": [{"table": "smartcityinitiatives", "columns": ["cropid", "race"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO spaceships (hotel_id, participant) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "spaceships", "columns": ["hotel_id", "participant"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"project_budget\").select([\"id\", \"amt\"]).copy_into(\"retail\").commit()\n", "labels": {"reads": [{"table": "project_budget", "columns": null}], "writes": [{"table": "retail", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO stg.stg_orders_df SELECT report_date, statement_id, dockingid FROM autonomous_vehicles WHERE report_date > 439;\nEOF\n", "labels": {"reads": [{"table": "autonomous_vehicles", "columns": ["report_date", "statement_id", "dockingid"]}], "writes": [{"table": "stg.stg_orders_df", "columns": ["report_date", "statement_id", "dockingid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT address_line_1, waste_type FROM dws.dws_orders_df LIMIT 310\")\nrows = cur.fetchall()\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "dws.dws_orders_df", "columns": ["address_line_1", "waste_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"safety_test_results\").where(\"dt = current_date()\")\nevents.writeTo(\"investigative_journalism\").append()\n", "labels": {"reads": [{"table": "safety_test_results", "columns": null}], "writes": [{"table": "investigative_journalism", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nset -euo pipefail\nsqoop import --connect \"$JDBC\" --table humanitarianassistance --target-dir /tmp/land\n", "labels": {"reads": [{"table": "humanitarianassistance", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"contractors\").where(\"dt = current_date()\")\nevents.writeTo(\"cargoships\").append()\n", "labels": {"reads": [{"table": "contractors", "columns": null}], "writes": [{"table": "cargoships", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"member_workout_date\")\nsrc.write.insertInto(\"mentalhealthparity\", overwrite=True)\n", "labels": {"reads": [{"table": "member_workout_date", "columns": null}], "writes": [{"table": "mentalhealthparity", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nsqoop import --connect \"$JDBC\" --table trench_depths --target-dir /tmp/land\n", "labels": {"reads": [{"table": "trench_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": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"tourism_impact\", limit=5000)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "tourism_impact", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO financial_institutions SELECT emission_date, away_team_three_point FROM organiccottongarments WHERE emission_date > 213\"\n", "labels": {"reads": [{"table": "organiccottongarments", "columns": ["emission_date", "away_team_three_point"]}], "writes": [{"table": "financial_institutions", "columns": ["emission_date", "away_team_three_point"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.product_type > 238).all()\n# src table: destinations\nengine.execute(\"INSERT INTO images SELECT * FROM destinations\")\n", "labels": {"reads": [{"table": "destinations", "columns": null}], "writes": [{"table": "images", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO community_development SELECT departmentid, patient_id, tech_type FROM ocean_basin WHERE departmentid > 261\"\n", "labels": {"reads": [{"table": "ocean_basin", "columns": ["departmentid", "patient_id", "tech_type"]}], "writes": [{"table": "community_development", "columns": ["departmentid", "patient_id", "tech_type"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dwd.member_point_df\", limit=5000)\nimport logging\n", "labels": {"reads": [{"table": "dwd.member_point_df", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO addresses SELECT savings, report_id, connection FROM cargoships WHERE savings > 296\"))\n", "labels": {"reads": [{"table": "cargoships", "columns": ["savings", "report_id", "connection"]}], "writes": [{"table": "addresses", "columns": ["savings", "report_id", "connection"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT event, nutrient_level FROM company_data LIMIT 154\")\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": "company_data", "columns": ["event", "nutrient_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO documents_with_expenses SELECT container_count, longitude, manufacturer_name, origin_city FROM stg.stg_exposure_df WHERE container_count > 497\"\n", "labels": {"reads": [{"table": "stg.stg_exposure_df", "columns": ["container_count", "longitude", "manufacturer_name", "origin_city"]}], "writes": [{"table": "documents_with_expenses", "columns": ["container_count", "longitude", "manufacturer_name", "origin_city"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO trip_segments SELECT energy_star_rating, location_id, how_to_get_there FROM stock_data WHERE energy_star_rating > 487\"))\n", "labels": {"reads": [{"table": "stock_data", "columns": ["energy_star_rating", "location_id", "how_to_get_there"]}], "writes": [{"table": "trip_segments", "columns": ["energy_star_rating", "location_id", "how_to_get_there"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"airports\").where(\"dt = current_date()\")\nevents.writeTo(\"crane\").append()\n", "labels": {"reads": [{"table": "airports", "columns": null}], "writes": [{"table": "crane", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model financial_wellbeing depends on workforce_diversity\ndbt run --select financial_wellbeing --vars '{\"src\":\"workforce_diversity\"}'\n", "labels": {"reads": [{"table": "workforce_diversity", "columns": null}], "writes": [{"table": "financial_wellbeing", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"artifactcounts\").where(\"dt = current_date()\")\nevents.writeTo(\"freight_forwarding\").append()\n", "labels": {"reads": [{"table": "artifactcounts", "columns": null}], "writes": [{"table": "freight_forwarding", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model products_hourly depends on age_groups\ndbt run --select products_hourly --vars '{\"src\":\"age_groups\"}'\n", "labels": {"reads": [{"table": "age_groups", "columns": null}], "writes": [{"table": "products_hourly", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"broadcast\")\nsrc.write.insertInto(\"lanthanummines\", overwrite=True)\n", "labels": {"reads": [{"table": "broadcast", "columns": null}], "writes": [{"table": "lanthanummines", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO dysprosium_production SELECT sales_count, max_depth, exhibitionname, num_virtual_tours FROM intangible_heritage WHERE sales_count > 479\"))\n", "labels": {"reads": [{"table": "intangible_heritage", "columns": ["sales_count", "max_depth", "exhibitionname", "num_virtual_tours"]}], "writes": [{"table": "dysprosium_production", "columns": ["sales_count", "max_depth", "exhibitionname", "num_virtual_tours"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model lines depends on production_quebec\ndbt run --select lines --vars '{\"src\":\"production_quebec\"}'\n", "labels": {"reads": [{"table": "production_quebec", "columns": null}], "writes": [{"table": "lines", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO instruments SELECT machine_id, equipment_id FROM ods.ods_member_point WHERE machine_id > 265\"\n", "labels": {"reads": [{"table": "ods.ods_member_point", "columns": ["machine_id", "equipment_id"]}], "writes": [{"table": "instruments", "columns": ["machine_id", "equipment_id"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO artdistribution SELECT vehicle_flight_number, completion_status, date_became_customer, opname FROM factory WHERE vehicle_flight_number > 422\"))\n", "labels": {"reads": [{"table": "factory", "columns": ["vehicle_flight_number", "completion_status", "date_became_customer", "opname"]}], "writes": [{"table": "artdistribution", "columns": ["vehicle_flight_number", "completion_status", "date_became_customer", "opname"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"supplier\").select([\"id\", \"amt\"]).copy_into(\"news_ratings\").commit()\n", "labels": {"reads": [{"table": "supplier", "columns": null}], "writes": [{"table": "news_ratings", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT mining_operation_id, apid FROM emergency_incidents LIMIT 48\")\nimport logging\nspark.sql(\"INSERT INTO lanthanummines SELECT project_education, model_id, service, creation FROM stg_campaigns_di WHERE project_education > 77\")\n", "labels": {"reads": [{"table": "emergency_incidents", "columns": ["mining_operation_id", "apid"]}, {"table": "stg_campaigns_di", "columns": ["project_education", "model_id", "service", "creation"]}], "writes": [{"table": "lanthanummines", "columns": ["project_education", "model_id", "service", "creation"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model personfriend depends on lending_initiatives\ndbt run --select personfriend --vars '{\"src\":\"lending_initiatives\"}'\n", "labels": {"reads": [{"table": "lending_initiatives", "columns": null}], "writes": [{"table": "personfriend", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model skills_required_to_fix depends on union_members_demographics\ndbt run --select skills_required_to_fix --vars '{\"src\":\"union_members_demographics\"}'\n", "labels": {"reads": [{"table": "union_members_demographics", "columns": null}], "writes": [{"table": "skills_required_to_fix", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"storm\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "storm", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"startup\")\nsrc.write.insertInto(\"community_education\", overwrite=True)\n", "labels": {"reads": [{"table": "startup", "columns": null}], "writes": [{"table": "community_education", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM mart.mart_risk_score_di\", conn)\ndf.to_sql(\"newssource\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "mart.mart_risk_score_di", "columns": null}], "writes": [{"table": "newssource", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO screenings SELECT sanctuary, customer_country, sport_id FROM dwd.dwd_campaigns_daily WHERE sanctuary > 148\"\n", "labels": {"reads": [{"table": "dwd.dwd_campaigns_daily", "columns": ["sanctuary", "customer_country", "sport_id"]}], "writes": [{"table": "screenings", "columns": ["sanctuary", "customer_country", "sport_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO playlist_tracks SELECT disease, heritage_site_id FROM sector WHERE disease > 433\"\n", "labels": {"reads": [{"table": "sector", "columns": ["disease", "heritage_site_id"]}], "writes": [{"table": "playlist_tracks", "columns": ["disease", "heritage_site_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO medical SELECT common_name, operation_type, satellite, hireid FROM recipe WHERE common_name > 323\"))\n", "labels": {"reads": [{"table": "recipe", "columns": ["common_name", "operation_type", "satellite", "hireid"]}], "writes": [{"table": "medical", "columns": ["common_name", "operation_type", "satellite", "hireid"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model news_ratings depends on stg.stg_exposure_df\ndbt run --select news_ratings --vars '{\"src\":\"stg.stg_exposure_df\"}'\n", "labels": {"reads": [{"table": "stg.stg_exposure_df", "columns": null}], "writes": [{"table": "news_ratings", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model stg_sessions_daily depends on museumart\ndbt run --select stg_sessions_daily --vars '{\"src\":\"museumart\"}'\n", "labels": {"reads": [{"table": "museumart", "columns": null}], "writes": [{"table": "stg_sessions_daily", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"contractors\").select([\"id\", \"amt\"]).copy_into(\"solar_installations\").commit()\n", "labels": {"reads": [{"table": "contractors", "columns": null}], "writes": [{"table": "solar_installations", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"moviebudgets\").select([\"id\", \"amt\"]).copy_into(\"physician\").commit()\n", "labels": {"reads": [{"table": "moviebudgets", "columns": null}], "writes": [{"table": "physician", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"restorative_justice\").toPandas()\ndf[[\"recruiterid\", \"doctorsper1000\"]].to_sql(\"smartcitysavings\", engine, index=False)\n", "labels": {"reads": [{"table": "restorative_justice", "columns": null}], "writes": [{"table": "smartcitysavings", "columns": ["recruiterid", "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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO community_education SELECT artworkname, game_id, dept_code FROM citizen_feedback_records WHERE artworkname > 168\"))\n", "labels": {"reads": [{"table": "citizen_feedback_records", "columns": ["artworkname", "game_id", "dept_code"]}], "writes": [{"table": "community_education", "columns": ["artworkname", "game_id", "dept_code"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"traveladvisoryreasons\").where(\"dt = current_date()\")\nevents.writeTo(\"dws.dws_sessions_df\").append()\n", "labels": {"reads": [{"table": "traveladvisoryreasons", "columns": null}], "writes": [{"table": "dws.dws_sessions_df", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model elements depends on pumped_hydro_projects\ndbt run --select elements --vars '{\"src\":\"pumped_hydro_projects\"}'\n", "labels": {"reads": [{"table": "pumped_hydro_projects", "columns": null}], "writes": [{"table": "elements", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM injuries\"\n", "labels": {"reads": [{"table": "injuries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"mascaras\").select([\"id\", \"amt\"]).copy_into(\"client\").commit()\n", "labels": {"reads": [{"table": "mascaras", "columns": null}], "writes": [{"table": "client", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO techniques SELECT projectid, silver, event_details, maxoccupancy FROM hotel_ai WHERE projectid > 241\"\n", "labels": {"reads": [{"table": "hotel_ai", "columns": ["projectid", "silver", "event_details", "maxoccupancy"]}], "writes": [{"table": "techniques", "columns": ["projectid", "silver", "event_details", "maxoccupancy"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO countryincome SELECT match_id, framework_id, purchase_details, observation_date FROM product_complaints WHERE match_id > 218\"\n", "labels": {"reads": [{"table": "product_complaints", "columns": ["match_id", "framework_id", "purchase_details", "observation_date"]}], "writes": [{"table": "countryincome", "columns": ["match_id", "framework_id", "purchase_details", "observation_date"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO elements (working_horses, oct) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "elements", "columns": ["working_horses", "oct"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dws.dws_exposure_df\", limit=5000)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "dws.dws_exposure_df", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"stg_exposure_delta\", writes=\"member_demographics\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "stg_exposure_delta", "columns": null}], "writes": [{"table": "member_demographics", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO ref_company_types SELECT provider_name, reason, artworkid, other_item_details FROM education_aid WHERE provider_name > 160\"\n", "labels": {"reads": [{"table": "education_aid", "columns": ["provider_name", "reason", "artworkid", "other_item_details"]}], "writes": [{"table": "ref_company_types", "columns": ["provider_name", "reason", "artworkid", "other_item_details"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"instruments\", writes=\"studentsmentalhealth\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "instruments", "columns": null}], "writes": [{"table": "studentsmentalhealth", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"dw_clicks_hourly\").select([\"id\", \"amt\"]).copy_into(\"ethereum_contracts\").commit()\n", "labels": {"reads": [{"table": "dw_clicks_hourly", "columns": null}], "writes": [{"table": "ethereum_contracts", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO nurse SELECT languageid, averagespeed, author_or_editor, attorney FROM buildingtypes WHERE languageid > 236\"\n", "labels": {"reads": [{"table": "buildingtypes", "columns": ["languageid", "averagespeed", "author_or_editor", "attorney"]}], "writes": [{"table": "nurse", "columns": ["languageid", "averagespeed", "author_or_editor", "attorney"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model education_aid depends on project_staff\ndbt run --select education_aid --vars '{\"src\":\"project_staff\"}'\n", "labels": {"reads": [{"table": "project_staff", "columns": null}], "writes": [{"table": "education_aid", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO ai_recs 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": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM hotel_ai\", conn)\ndf.to_sql(\"sustainable_buildings\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "hotel_ai", "columns": null}], "writes": [{"table": "sustainable_buildings", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO dws.dws_inventory_full SELECT customer_email_address, gender_mf, casestatus FROM actual_orders WHERE customer_email_address > 219\")\n", "labels": {"reads": [{"table": "actual_orders", "columns": ["customer_email_address", "gender_mf", "casestatus"]}], "writes": [{"table": "dws.dws_inventory_full", "columns": ["customer_email_address", "gender_mf", "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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ai_recs SELECT model_year, movie, stu_num, meter_200 FROM therapeutic_areas WHERE model_year > 91\"\n", "labels": {"reads": [{"table": "therapeutic_areas", "columns": ["model_year", "movie", "stu_num", "meter_200"]}], "writes": [{"table": "ai_recs", "columns": ["model_year", "movie", "stu_num", "meter_200"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO studentsmentalhealth SELECT feedid, consider_rate, date_assigned_from, task_id FROM mlb_teams_mascots WHERE feedid > 106\"\n", "labels": {"reads": [{"table": "mlb_teams_mascots", "columns": ["feedid", "consider_rate", "date_assigned_from", "task_id"]}], "writes": [{"table": "studentsmentalhealth", "columns": ["feedid", "consider_rate", "date_assigned_from", "task_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": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT treatment_id, consultations FROM eco_accommodations LIMIT 173;\nINSERT INTO artist_genre SELECT chair_name, section_id FROM arctic_vessels WHERE chair_name > 336;\nSQL\n", "labels": {"reads": [{"table": "eco_accommodations", "columns": ["treatment_id", "consultations"]}, {"table": "arctic_vessels", "columns": ["chair_name", "section_id"]}], "writes": [{"table": "artist_genre", "columns": ["chair_name", "section_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO classes SELECT profession, interaction_type FROM vulnerabilityassessments WHERE profession > 110\"\n", "labels": {"reads": [{"table": "vulnerabilityassessments", "columns": ["profession", "interaction_type"]}], "writes": [{"table": "classes", "columns": ["profession", "interaction_type"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"mining_sites\", writes=\"europe_org\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "mining_sites", "columns": null}], "writes": [{"table": "europe_org", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model transactions depends on donors\ndbt run --select transactions --vars '{\"src\":\"donors\"}'\n", "labels": {"reads": [{"table": "donors", "columns": null}], "writes": [{"table": "transactions", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"airports\")\nsrc.write.insertInto(\"energy_efficiency\", overwrite=True)\n", "labels": {"reads": [{"table": "airports", "columns": null}], "writes": [{"table": "energy_efficiency", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"visitor_demographics\", limit=5000)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "visitor_demographics", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nsql = \"INSERT INTO mining_company_revenue SELECT a.department_id, b.reign FROM endangered_species a JOIN elements b ON a.document_name = b.document_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "endangered_species", "columns": null}, {"table": "elements", "columns": null}], "writes": [{"table": "mining_company_revenue", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT customername, strategy FROM studentsmentalhealth\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"ods.ods_products\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "studentsmentalhealth", "columns": ["customername", "strategy"]}], "writes": [{"table": "ods.ods_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"lifelong_learning\").where(\"dt = current_date()\")\nevents.writeTo(\"dws.dws_sessions_df\").append()\n", "labels": {"reads": [{"table": "lifelong_learning", "columns": null}], "writes": [{"table": "dws.dws_sessions_df", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO memberships SELECT species_id, wildlife_type_id FROM bi.products_hourly WHERE species_id > 375\"))\n", "labels": {"reads": [{"table": "bi.products_hourly", "columns": ["species_id", "wildlife_type_id"]}], "writes": [{"table": "memberships", "columns": ["species_id", "wildlife_type_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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": "heldout"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nset -euo pipefail\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table sector --target-dir /tmp/land\n", "labels": {"reads": [{"table": "sector", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ads.ads_users_delta\", writes=\"autonomous_taxis\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ads.ads_users_delta", "columns": null}], "writes": [{"table": "autonomous_taxis", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"resourcemanagement\").select([\"id\", \"amt\"]).copy_into(\"user_check_ins\").commit()\n", "labels": {"reads": [{"table": "resourcemanagement", "columns": null}], "writes": [{"table": "user_check_ins", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO aquaculture_zones SELECT unique_founders, rural_area, culturalcompetency FROM treatment WHERE unique_founders > 464\"\n", "labels": {"reads": [{"table": "treatment", "columns": ["unique_founders", "rural_area", "culturalcompetency"]}], "writes": [{"table": "aquaculture_zones", "columns": ["unique_founders", "rural_area", "culturalcompetency"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT gameid, task_details FROM oregondispensaries\", engine)\nmetrics.append(round(score, 4))\ndf.to_sql(\"country\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["gameid", "task_details"]}], "writes": [{"table": "country", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"church\", writes=\"classes\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "church", "columns": null}], "writes": [{"table": "classes", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"content\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "content", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"unique_donors\").select([\"id\", \"amt\"]).copy_into(\"playlist_tracks\").commit()\n", "labels": {"reads": [{"table": "unique_donors", "columns": null}], "writes": [{"table": "playlist_tracks", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM agroecology\"\n", "labels": {"reads": [{"table": "agroecology", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"tech_transactions\").select([\"id\", \"amt\"]).copy_into(\"project_outcomes\").commit()\n", "labels": {"reads": [{"table": "tech_transactions", "columns": null}], "writes": [{"table": "project_outcomes", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO platforms SELECT a.hotel_id, b.creation FROM elections a JOIN gamerevenue b ON a.local_authority = b.local_authority\"\n", "labels": {"reads": [{"table": "elections", "columns": null}, {"table": "gamerevenue", "columns": null}], "writes": [{"table": "platforms", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nexport TZ=Asia/Shanghai\ntrap 'echo failed' ERR\nsqoop import --connect \"$JDBC\" --table dept_locations --target-dir /tmp/land\n", "labels": {"reads": [{"table": "dept_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"clientinvestments\").where(\"dt = current_date()\")\nevents.writeTo(\"emergency_incidents\").append()\n", "labels": {"reads": [{"table": "clientinvestments", "columns": null}], "writes": [{"table": "emergency_incidents", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO dws.dws_exposure_df SELECT feb, units_sold FROM broadcast WHERE feb > 194\"\n", "labels": {"reads": [{"table": "broadcast", "columns": ["feb", "units_sold"]}], "writes": [{"table": "dws.dws_exposure_df", "columns": ["feb", "units_sold"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"lawprograms\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"healthcare_providers\")\n", "labels": {"reads": [{"table": "lawprograms", "columns": null}], "writes": [{"table": "healthcare_providers", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model london_train_trips depends on criminal_database\ndbt run --select london_train_trips --vars '{\"src\":\"criminal_database\"}'\n", "labels": {"reads": [{"table": "criminal_database", "columns": null}], "writes": [{"table": "london_train_trips", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"farm_soil_moisture\").select([\"id\", \"amt\"]).copy_into(\"studentsmentalhealth\").commit()\n", "labels": {"reads": [{"table": "farm_soil_moisture", "columns": null}], "writes": [{"table": "studentsmentalhealth", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO dws_shipments SELECT improvement, workforce_development, enrollment_date, carrierid FROM oregondispensaries WHERE improvement > 462\"\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["improvement", "workforce_development", "enrollment_date", "carrierid"]}], "writes": [{"table": "dws_shipments", "columns": ["improvement", "workforce_development", "enrollment_date", "carrierid"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model dw_clicks_hourly depends on mining_company_revenue\ndbt run --select dw_clicks_hourly --vars '{\"src\":\"mining_company_revenue\"}'\n", "labels": {"reads": [{"table": "mining_company_revenue", "columns": null}], "writes": [{"table": "dw_clicks_hourly", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ads.ads_coupon_use SELECT call_count, journal_id, menuid FROM inspections_tx WHERE call_count > 185\"\n", "labels": {"reads": [{"table": "inspections_tx", "columns": ["call_count", "journal_id", "menuid"]}], "writes": [{"table": "ads.ads_coupon_use", "columns": ["call_count", "journal_id", "menuid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT booked_count, team_name FROM production_quebec LIMIT 256;\nINSERT INTO member_workouts SELECT status, course_type, org FROM hospital_beds WHERE status > 1;\nSQL\n", "labels": {"reads": [{"table": "production_quebec", "columns": ["booked_count", "team_name"]}, {"table": "hospital_beds", "columns": ["status", "course_type", "org"]}], "writes": [{"table": "member_workouts", "columns": ["status", "course_type", "org"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO dwd.device_log_daily (rating_id, distributorid) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "dwd.device_log_daily", "columns": ["rating_id", "distributorid"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO documents_mailed SELECT party, customer_code, statement_id FROM order_details WHERE party > 383\")\n", "labels": {"reads": [{"table": "order_details", "columns": ["party", "customer_code", "statement_id"]}], "writes": [{"table": "documents_mailed", "columns": ["party", "customer_code", "statement_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": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"vendor\", limit=5000)\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "vendor", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"studentsmentalhealth\").where(\"dt = current_date()\")\nevents.writeTo(\"public_buildings\").append()\n", "labels": {"reads": [{"table": "studentsmentalhealth", "columns": null}], "writes": [{"table": "public_buildings", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model peacekeeping_operations depends on sales.games\ndbt run --select peacekeeping_operations --vars '{\"src\":\"sales.games\"}'\n", "labels": {"reads": [{"table": "sales.games", "columns": null}], "writes": [{"table": "peacekeeping_operations", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"taxi_occupancy\").select([\"id\", \"amt\"]).copy_into(\"things\").commit()\n", "labels": {"reads": [{"table": "taxi_occupancy", "columns": null}], "writes": [{"table": "things", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"performance\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "performance", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT community_name, operation_type FROM farmwatertemp LIMIT 159\")\nlogger = logging.getLogger(__name__)\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO ma_restaurants SELECT financing_date, satelliteid, cultivatorname, principal_activities FROM mouse WHERE financing_date > 472\")\n", "labels": {"reads": [{"table": "farmwatertemp", "columns": ["community_name", "operation_type"]}, {"table": "mouse", "columns": ["financing_date", "satelliteid", "cultivatorname", "principal_activities"]}], "writes": [{"table": "ma_restaurants", "columns": ["financing_date", "satelliteid", "cultivatorname", "principal_activities"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"farmland\", writes=\"dws.dws_inventory_full\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "farmland", "columns": null}], "writes": [{"table": "dws.dws_inventory_full", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"culture_company\", limit=5000)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "culture_company", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO football_teams 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": "heldout"}}
{"task_type": "SHELL", "content": "# model esports_participants depends on intangible_heritage\ndbt run --select esports_participants --vars '{\"src\":\"intangible_heritage\"}'\n", "labels": {"reads": [{"table": "intangible_heritage", "columns": null}], "writes": [{"table": "esports_participants", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table ai_adoption --columns sales_details,i_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "ai_adoption", "columns": ["sales_details", "i_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": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO recyclingprograms SELECT issue, support_rate, reports_to FROM mining_activities WHERE issue > 296\"], check=True)\n", "labels": {"reads": [{"table": "mining_activities", "columns": ["issue", "support_rate", "reports_to"]}], "writes": [{"table": "recyclingprograms", "columns": ["issue", "support_rate", "reports_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"mart.mart_risk_score_di\").where(\"dt = current_date()\")\nevents.writeTo(\"dws_shipments\").append()\n", "labels": {"reads": [{"table": "mart.mart_risk_score_di", "columns": null}], "writes": [{"table": "dws_shipments", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO dws.exposure_hourly SELECT 1\"\nlogger.info(msg)\nresult = value * ratio + offset\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": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table jobopenings --columns tree_id,scooter_id --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "jobopenings", "columns": ["tree_id", "scooter_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"journalist\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"crane\")\n", "labels": {"reads": [{"table": "journalist", "columns": null}], "writes": [{"table": "crane", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO studentmentalhealth SELECT department, workout_type, courtid FROM ethical_ai_courses_year WHERE department > 228\"\n", "labels": {"reads": [{"table": "ethical_ai_courses_year", "columns": ["department", "workout_type", "courtid"]}], "writes": [{"table": "studentmentalhealth", "columns": ["department", "workout_type", "courtid"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"debate\").select([\"id\", \"amt\"]).copy_into(\"attorneys\").commit()\n", "labels": {"reads": [{"table": "debate", "columns": null}], "writes": [{"table": "attorneys", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"co_ownerships\").where(\"dt = current_date()\")\nevents.writeTo(\"autonomous_vehicles\").append()\n", "labels": {"reads": [{"table": "co_ownerships", "columns": null}], "writes": [{"table": "autonomous_vehicles", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"unique_donors\", limit=5000)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "unique_donors", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO london.lines SELECT visitor_country, coupon_id, partitionid, condition_id FROM flight WHERE visitor_country > 449\"))\n", "labels": {"reads": [{"table": "flight", "columns": ["visitor_country", "coupon_id", "partitionid", "condition_id"]}], "writes": [{"table": "london.lines", "columns": ["visitor_country", "coupon_id", "partitionid", "condition_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"animaleducation\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "animaleducation", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"freight\", writes=\"explainability_report\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "freight", "columns": null}], "writes": [{"table": "explainability_report", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"explainability_report\").where(\"dt = current_date()\")\nevents.writeTo(\"sustainable_urbanism\").append()\n", "labels": {"reads": [{"table": "explainability_report", "columns": null}], "writes": [{"table": "sustainable_urbanism", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"flu_vaccinations\").select([\"id\", \"amt\"]).copy_into(\"crop\").commit()\n", "labels": {"reads": [{"table": "flu_vaccinations", "columns": null}], "writes": [{"table": "crop", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"document_drafts\").where(\"dt = current_date()\")\nevents.writeTo(\"union_members_demographics\").append()\n", "labels": {"reads": [{"table": "document_drafts", "columns": null}], "writes": [{"table": "union_members_demographics", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"building_data\").toPandas()\ndf[[\"founded\", \"market\"]].to_sql(\"student_records\", engine, index=False)\n", "labels": {"reads": [{"table": "building_data", "columns": null}], "writes": [{"table": "student_records", "columns": ["founded", "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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO farm_soil_moisture SELECT points_per_game, lat, trip_id, offer_id FROM algorithm_fairness WHERE points_per_game > 196\"))\n", "labels": {"reads": [{"table": "algorithm_fairness", "columns": ["points_per_game", "lat", "trip_id", "offer_id"]}], "writes": [{"table": "farm_soil_moisture", "columns": ["points_per_game", "lat", "trip_id", "offer_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO skincare_ingredients SELECT generation_date, patient_age FROM incident_regions WHERE generation_date > 345\"\n", "labels": {"reads": [{"table": "incident_regions", "columns": ["generation_date", "patient_age"]}], "writes": [{"table": "skincare_ingredients", "columns": ["generation_date", "patient_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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO destinations SELECT vrdevice, zip, continent_id FROM dws.dws_cart_item_di WHERE vrdevice > 380\"\n", "labels": {"reads": [{"table": "dws.dws_cart_item_di", "columns": ["vrdevice", "zip", "continent_id"]}], "writes": [{"table": "destinations", "columns": ["vrdevice", "zip", "continent_id"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model room depends on delivery_routes\ndbt run --select room --vars '{\"src\":\"delivery_routes\"}'\n", "labels": {"reads": [{"table": "delivery_routes", "columns": null}], "writes": [{"table": "room", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO mart.sessions_hourly SELECT stu_gpa, prod_id, away_team_points FROM graduatestudents WHERE stu_gpa > 383\"], check=True)\n", "labels": {"reads": [{"table": "graduatestudents", "columns": ["stu_gpa", "prod_id", "away_team_points"]}], "writes": [{"table": "mart.sessions_hourly", "columns": ["stu_gpa", "prod_id", "away_team_points"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO member_demographics SELECT * FROM legacy\ncur.execute(\"SELECT development_type, evaluated_for_fairness FROM smart_cities.ev_charging_stations LIMIT 323\")\n", "labels": {"reads": [{"table": "smart_cities.ev_charging_stations", "columns": ["development_type", "evaluated_for_fairness"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"extractiondata\", writes=\"billing\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "extractiondata", "columns": null}], "writes": [{"table": "billing", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO destinations SELECT material_type, shipmenttype, don_name FROM member_demographics WHERE material_type > 482\"))\n", "labels": {"reads": [{"table": "member_demographics", "columns": ["material_type", "shipmenttype", "don_name"]}], "writes": [{"table": "destinations", "columns": ["material_type", "shipmenttype", "don_name"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"heritage_tours\", writes=\"resourcemanagement\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "heritage_tours", "columns": null}], "writes": [{"table": "resourcemanagement", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ai_recs\"\n", "labels": {"reads": [{"table": "ai_recs", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO delivery_routes SELECT opening_year, workout_id FROM autonomous_vehicles WHERE opening_year > 487\"))\n", "labels": {"reads": [{"table": "autonomous_vehicles", "columns": ["opening_year", "workout_id"]}], "writes": [{"table": "delivery_routes", "columns": ["opening_year", "workout_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"church\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "church", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"bi.cart_item\", limit=5000)\nlogger = logging.getLogger(__name__)\nimport logging\n", "labels": {"reads": [{"table": "bi.cart_item", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table trench_depths --columns release_year,crs_code --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "trench_depths", "columns": ["release_year", "crs_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainablepractices\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"unique_donors\")\n", "labels": {"reads": [{"table": "sustainablepractices", "columns": null}], "writes": [{"table": "unique_donors", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO individuals 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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO artdistribution SELECT pilot_id, sustainabilityid, shipmenttype, healthcareid FROM gas_processing_plants WHERE pilot_id > 177\"\n", "labels": {"reads": [{"table": "gas_processing_plants", "columns": ["pilot_id", "sustainabilityid", "shipmenttype", "healthcareid"]}], "writes": [{"table": "artdistribution", "columns": ["pilot_id", "sustainabilityid", "shipmenttype", "healthcareid"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"mart.mart_risk_score_di\").select([\"id\", \"amt\"]).copy_into(\"ref_document_types\").commit()\n", "labels": {"reads": [{"table": "mart.mart_risk_score_di", "columns": null}], "writes": [{"table": "ref_document_types", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"satellite_images\", limit=5000)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "satellite_images", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO mobile_subscribers_roaming SELECT winning_pilot, phone, resolutiondate FROM sleep WHERE winning_pilot > 82\"\n", "labels": {"reads": [{"table": "sleep", "columns": ["winning_pilot", "phone", "resolutiondate"]}], "writes": [{"table": "mobile_subscribers_roaming", "columns": ["winning_pilot", "phone", "resolutiondate"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"memberships\", limit=5000)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "memberships", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"carriers\", limit=5000)\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "carriers", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO media.reporters SELECT numcases, uid FROM loans WHERE numcases > 242\"))\n", "labels": {"reads": [{"table": "loans", "columns": ["numcases", "uid"]}], "writes": [{"table": "media.reporters", "columns": ["numcases", "uid"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"manufacturer_sales\", writes=\"claim_3\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "manufacturer_sales", "columns": null}], "writes": [{"table": "claim_3", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO ocean_basin SELECT number_of_sightings, discount, permitdate, exoplanet FROM humanitarianassistance WHERE number_of_sightings > 272\"\n", "labels": {"reads": [{"table": "humanitarianassistance", "columns": ["number_of_sightings", "discount", "permitdate", "exoplanet"]}], "writes": [{"table": "ocean_basin", "columns": ["number_of_sightings", "discount", "permitdate", "exoplanet"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT date_account_opened, manufacturer_name FROM deliveries\", engine)\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\ndf.to_sql(\"allergy_type\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "deliveries", "columns": ["date_account_opened", "manufacturer_name"]}], "writes": [{"table": "allergy_type", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"language_preservation\", limit=5000)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "language_preservation", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ethical_ai_courses_year SELECT exhibitionname, tree_type_id FROM donationdates WHERE exhibitionname > 234\"\n", "labels": {"reads": [{"table": "donationdates", "columns": ["exhibitionname", "tree_type_id"]}], "writes": [{"table": "ethical_ai_courses_year", "columns": ["exhibitionname", "tree_type_id"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"unique_donors\", writes=\"energy_efficiency_stats\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "unique_donors", "columns": null}], "writes": [{"table": "energy_efficiency_stats", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"nonprofits\").select([\"id\", \"amt\"]).copy_into(\"union_stats\").commit()\n", "labels": {"reads": [{"table": "nonprofits", "columns": null}], "writes": [{"table": "union_stats", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO vehicle_safety SELECT attraction_type_description, guest_last_name FROM moviebudgets WHERE attraction_type_description > 445\"\n", "labels": {"reads": [{"table": "moviebudgets", "columns": ["attraction_type_description", "guest_last_name"]}], "writes": [{"table": "vehicle_safety", "columns": ["attraction_type_description", "guest_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = pull_table(ctx, \"media_content\")\npush_to_output(df, \"treatment_type\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "media_content", "columns": null}], "writes": [{"table": "treatment_type", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO cargoships SELECT product_name, dishname FROM food WHERE product_name > 225\"\n", "labels": {"reads": [{"table": "food", "columns": ["product_name", "dishname"]}], "writes": [{"table": "cargoships", "columns": ["product_name", "dishname"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO worker_salaries SELECT 1\"\ntrap 'echo failed' ERR\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": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dissolved_oxygen_readings\", limit=5000)\nif not rows:\n    logger.warning('empty result')\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "dissolved_oxygen_readings", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"performance\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"visitor_stats\")\n", "labels": {"reads": [{"table": "performance", "columns": null}], "writes": [{"table": "visitor_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": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"company_data\", writes=\"dept_locations\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "company_data", "columns": null}], "writes": [{"table": "dept_locations", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nimport logging\nsql = \"INSERT INTO budgetallocation SELECT a.statement_details, b.seat_section FROM club_rank a JOIN artdistribution b ON a.organisation_id = b.organisation_id\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "club_rank", "columns": null}, {"table": "artdistribution", "columns": null}], "writes": [{"table": "budgetallocation", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO donationcategories 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": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT lastdonationdate, chemical_id FROM memberships LIMIT 424\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "memberships", "columns": ["lastdonationdate", "chemical_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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ads_exposure_daily SELECT premises_type, region, opened_date FROM startup WHERE premises_type > 295\"\n", "labels": {"reads": [{"table": "startup", "columns": ["premises_type", "region", "opened_date"]}], "writes": [{"table": "ads_exposure_daily", "columns": ["premises_type", "region", "opened_date"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"policies\", writes=\"treatments\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "policies", "columns": null}], "writes": [{"table": "treatments", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ods.ods_member_point SELECT price, mean_sea_level_pressure_inches, co2_reduction_tons FROM resourcemanagement WHERE price > 114\"\n", "labels": {"reads": [{"table": "resourcemanagement", "columns": ["price", "mean_sea_level_pressure_inches", "co2_reduction_tons"]}], "writes": [{"table": "ods.ods_member_point", "columns": ["price", "mean_sea_level_pressure_inches", "co2_reduction_tons"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO fleet_vessels SELECT fate, statename, team_id_br FROM habitat1 WHERE fate > 219\"))\n", "labels": {"reads": [{"table": "habitat1", "columns": ["fate", "statename", "team_id_br"]}], "writes": [{"table": "fleet_vessels", "columns": ["fate", "statename", "team_id_br"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO budgetallocation SELECT individual_name, nationality FROM mature_forest WHERE individual_name > 115\"))\n", "labels": {"reads": [{"table": "mature_forest", "columns": ["individual_name", "nationality"]}], "writes": [{"table": "budgetallocation", "columns": ["individual_name", "nationality"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model lanthanummines depends on eu_ets\ndbt run --select lanthanummines --vars '{\"src\":\"eu_ets\"}'\n", "labels": {"reads": [{"table": "eu_ets", "columns": null}], "writes": [{"table": "lanthanummines", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ads.exposure_daily\").toPandas()\ndf[[\"applicant\", \"feature_details\"]].to_sql(\"construction_workers\", engine, index=False)\n", "labels": {"reads": [{"table": "ads.exposure_daily", "columns": null}], "writes": [{"table": "construction_workers", "columns": ["applicant", "feature_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": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT service_details, bedroom_count FROM marine_trenches LIMIT 31;\nINSERT INTO sessions SELECT organization_details, transportation_method, order_date, friend FROM fleet_vessels WHERE organization_details > 97;\nSQL\n", "labels": {"reads": [{"table": "marine_trenches", "columns": ["service_details", "bedroom_count"]}, {"table": "fleet_vessels", "columns": ["organization_details", "transportation_method", "order_date", "friend"]}], "writes": [{"table": "sessions", "columns": ["organization_details", "transportation_method", "order_date", "friend"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"skills_required_to_fix\").toPandas()\ndf[[\"dispensary_id\", \"installation_year\"]].to_sql(\"ai_applications\", engine, index=False)\n", "labels": {"reads": [{"table": "skills_required_to_fix", "columns": null}], "writes": [{"table": "ai_applications", "columns": ["dispensary_id", "installation_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO dept_locations SELECT low_income_neighborhood, committee FROM salesrevenue WHERE low_income_neighborhood > 131\"\n", "labels": {"reads": [{"table": "salesrevenue", "columns": ["low_income_neighborhood", "committee"]}], "writes": [{"table": "dept_locations", "columns": ["low_income_neighborhood", "committee"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO culture_company SELECT update_date, engagement, daily_hire_cost FROM oregondispensaries WHERE update_date > 385\"\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["update_date", "engagement", "daily_hire_cost"]}], "writes": [{"table": "culture_company", "columns": ["update_date", "engagement", "daily_hire_cost"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT rid, watertemp FROM energy_efficiency LIMIT 151;\nINSERT INTO safetyrecord SELECT application_date, safety_id FROM public_buses WHERE application_date > 90;\nSQL\n", "labels": {"reads": [{"table": "energy_efficiency", "columns": ["rid", "watertemp"]}, {"table": "public_buses", "columns": ["application_date", "safety_id"]}], "writes": [{"table": "safetyrecord", "columns": ["application_date", "safety_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": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT building_address, project_type FROM csu_fees LIMIT 31\")\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\nretries = int(os.environ.get('RETRIES', '3'))\nspark.sql(\"INSERT INTO enroll SELECT production_quantity, adults, dept_id, sustainability_score FROM community_development.schools WHERE production_quantity > 490\")\n", "labels": {"reads": [{"table": "csu_fees", "columns": ["building_address", "project_type"]}, {"table": "community_development.schools", "columns": ["production_quantity", "adults", "dept_id", "sustainability_score"]}], "writes": [{"table": "enroll", "columns": ["production_quantity", "adults", "dept_id", "sustainability_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"co_ownerships\").select([\"id\", \"amt\"]).copy_into(\"recyclingprograms\").commit()\n", "labels": {"reads": [{"table": "co_ownerships", "columns": null}], "writes": [{"table": "recyclingprograms", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ods.ods_member_point SELECT zone, asset_acquired_date FROM europe_org WHERE zone > 38\"\n", "labels": {"reads": [{"table": "europe_org", "columns": ["zone", "asset_acquired_date"]}], "writes": [{"table": "ods.ods_member_point", "columns": ["zone", "asset_acquired_date"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO ocean_basin 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": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO rural_health_centers SELECT directed_by, site FROM algorithm_fairness WHERE directed_by > 86;\nEOF\n", "labels": {"reads": [{"table": "algorithm_fairness", "columns": ["directed_by", "site"]}], "writes": [{"table": "rural_health_centers", "columns": ["directed_by", "site"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO studentmentalhealth SELECT shipping_agent_code, quantity_sold, council_tax_id, request FROM medical_staff WHERE shipping_agent_code > 423\"], check=True)\n", "labels": {"reads": [{"table": "medical_staff", "columns": ["shipping_agent_code", "quantity_sold", "council_tax_id", "request"]}], "writes": [{"table": "studentmentalhealth", "columns": ["shipping_agent_code", "quantity_sold", "council_tax_id", "request"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO residents SELECT genreid, organization_name, movement FROM dws.dws_sessions_df WHERE genreid > 426\"))\n", "labels": {"reads": [{"table": "dws.dws_sessions_df", "columns": ["genreid", "organization_name", "movement"]}], "writes": [{"table": "residents", "columns": ["genreid", "organization_name", "movement"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ruralinfrastructure\", writes=\"retail_union\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ruralinfrastructure", "columns": null}], "writes": [{"table": "retail_union", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model ocean_trenches depends on renewable_energy\ndbt run --select ocean_trenches --vars '{\"src\":\"renewable_energy\"}'\n", "labels": {"reads": [{"table": "renewable_energy", "columns": null}], "writes": [{"table": "ocean_trenches", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "threshold = cfg.get('threshold', 0.5)\nimport logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO salmon_farms SELECT a.issues, b.budget_type_description FROM tech_companies a JOIN daily_transactions b ON a.park_name = b.park_name\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "tech_companies", "columns": null}, {"table": "daily_transactions", "columns": null}], "writes": [{"table": "salmon_farms", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"therapeutic_areas\", writes=\"research_outcomes\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "therapeutic_areas", "columns": null}], "writes": [{"table": "research_outcomes", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO property_sales SELECT start_speed, hoursspent FROM detention WHERE start_speed > 68\"\n", "labels": {"reads": [{"table": "detention", "columns": ["start_speed", "hoursspent"]}], "writes": [{"table": "property_sales", "columns": ["start_speed", "hoursspent"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"sleep\").select([\"id\", \"amt\"]).copy_into(\"digital_divide\").commit()\n", "labels": {"reads": [{"table": "sleep", "columns": null}], "writes": [{"table": "digital_divide", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model treatment_type depends on bi.cart_item\ndbt run --select treatment_type --vars '{\"src\":\"bi.cart_item\"}'\n", "labels": {"reads": [{"table": "bi.cart_item", "columns": null}], "writes": [{"table": "treatment_type", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO users SELECT treatment_year, allocation_date, humidity, council_tax_id FROM moviebudgets WHERE treatment_year > 105\"))\n", "labels": {"reads": [{"table": "moviebudgets", "columns": ["treatment_year", "allocation_date", "humidity", "council_tax_id"]}], "writes": [{"table": "users", "columns": ["treatment_year", "allocation_date", "humidity", "council_tax_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"oceans\").select([\"id\", \"amt\"]).copy_into(\"dw.campaigns\").commit()\n", "labels": {"reads": [{"table": "oceans", "columns": null}], "writes": [{"table": "dw.campaigns", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM ods.shipments_daily\"\n", "labels": {"reads": [{"table": "ods.shipments_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO clients SELECT jun, count_date FROM cultural_sites WHERE jun > 188\"\n", "labels": {"reads": [{"table": "cultural_sites", "columns": ["jun", "count_date"]}], "writes": [{"table": "clients", "columns": ["jun", "count_date"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"dws.dws_sessions_df\").select([\"id\", \"amt\"]).copy_into(\"company_data\").commit()\n", "labels": {"reads": [{"table": "dws.dws_sessions_df", "columns": null}], "writes": [{"table": "company_data", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO medication SELECT fabric_id, pipeline_name, provider_name FROM healthcare_access WHERE fabric_id > 27\"))\n", "labels": {"reads": [{"table": "healthcare_access", "columns": ["fabric_id", "pipeline_name", "provider_name"]}], "writes": [{"table": "medication", "columns": ["fabric_id", "pipeline_name", "provider_name"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"renewable.projects\").select([\"id\", \"amt\"]).copy_into(\"clientinvestments\").commit()\n", "labels": {"reads": [{"table": "renewable.projects", "columns": null}], "writes": [{"table": "clientinvestments", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"project_staff\", writes=\"medical_staff\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "project_staff", "columns": null}], "writes": [{"table": "medical_staff", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO museum_artists (cargo_weight, maintenance_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "museum_artists", "columns": ["cargo_weight", "maintenance_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"smartcitysavings\").where(\"dt = current_date()\")\nevents.writeTo(\"ods.ods_exposure\").append()\n", "labels": {"reads": [{"table": "smartcitysavings", "columns": null}], "writes": [{"table": "ods.ods_exposure", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ref_company_types\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "ref_company_types", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "export TZ=Asia/Shanghai\nset -euo pipefail\necho \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table lawprograms --target-dir /tmp/land\n", "labels": {"reads": [{"table": "lawprograms", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO recyclingamount SELECT don_name, organisation_details, outcome_date FROM quick_service.menu_items WHERE don_name > 430\"\n", "labels": {"reads": [{"table": "quick_service.menu_items", "columns": ["don_name", "organisation_details", "outcome_date"]}], "writes": [{"table": "recyclingamount", "columns": ["don_name", "organisation_details", "outcome_date"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"virtual_tours_usa\").select([\"id\", \"amt\"]).copy_into(\"stg.stg_refunds_hourly\").commit()\n", "labels": {"reads": [{"table": "virtual_tours_usa", "columns": null}], "writes": [{"table": "stg.stg_refunds_hourly", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.siteid > 25).all()\n# src table: attractions\nengine.execute(\"INSERT INTO visitor_demographics SELECT * FROM attractions\")\n", "labels": {"reads": [{"table": "attractions", "columns": null}], "writes": [{"table": "visitor_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": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO statements SELECT * FROM legacy\ncur.execute(\"SELECT founder_identifies_as_lgbtq, tree_species FROM heritage_tours LIMIT 147\")\n", "labels": {"reads": [{"table": "heritage_tours", "columns": ["founder_identifies_as_lgbtq", "tree_species"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"renewable.projects\").where(\"dt = current_date()\")\nevents.writeTo(\"tour_types\").append()\n", "labels": {"reads": [{"table": "renewable.projects", "columns": null}], "writes": [{"table": "tour_types", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT invoice_date, check_in_id FROM student_records\", engine)\nlogger = logging.getLogger(__name__)\nresult = value * ratio + offset\ndf.to_sql(\"safety_test_results\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "student_records", "columns": ["invoice_date", "check_in_id"]}], "writes": [{"table": "safety_test_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"startup\").select([\"id\", \"amt\"]).copy_into(\"climate\").commit()\n", "labels": {"reads": [{"table": "startup", "columns": null}], "writes": [{"table": "climate", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM csu_fees\", conn)\ndf.to_sql(\"ethereum_contracts\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "csu_fees", "columns": null}], "writes": [{"table": "ethereum_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": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT active_from_date, media_literacy_score FROM clientinvestments LIMIT 451;\nINSERT INTO intangible_heritage SELECT followers, album_id FROM micro_mobility WHERE followers > 430;\nSQL\n", "labels": {"reads": [{"table": "clientinvestments", "columns": ["active_from_date", "media_literacy_score"]}, {"table": "micro_mobility", "columns": ["followers", "album_id"]}], "writes": [{"table": "intangible_heritage", "columns": ["followers", "album_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"likes\").where(\"dt = current_date()\")\nevents.writeTo(\"sessions\").append()\n", "labels": {"reads": [{"table": "likes", "columns": null}], "writes": [{"table": "sessions", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO broadcast SELECT environmental_impact_score, conservation_status FROM sessions WHERE environmental_impact_score > 429\"\n", "labels": {"reads": [{"table": "sessions", "columns": ["environmental_impact_score", "conservation_status"]}], "writes": [{"table": "broadcast", "columns": ["environmental_impact_score", "conservation_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": "heldout"}}
{"task_type": "SHELL", "content": "# model volunteer_programs depends on threat_intelligence_data\ndbt run --select volunteer_programs --vars '{\"src\":\"threat_intelligence_data\"}'\n", "labels": {"reads": [{"table": "threat_intelligence_data", "columns": null}], "writes": [{"table": "volunteer_programs", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"co_ownerships\", writes=\"dwd.device_log_daily\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "co_ownerships", "columns": null}], "writes": [{"table": "dwd.device_log_daily", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO transactions SELECT weight, product_name FROM ods.payments_hourly WHERE weight > 7\"\n", "labels": {"reads": [{"table": "ods.payments_hourly", "columns": ["weight", "product_name"]}], "writes": [{"table": "transactions", "columns": ["weight", "product_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": "heldout"}}
{"task_type": "SHELL", "content": "# model gas_processing_plants depends on news_ratings\ndbt run --select gas_processing_plants --vars '{\"src\":\"news_ratings\"}'\n", "labels": {"reads": [{"table": "news_ratings", "columns": null}], "writes": [{"table": "gas_processing_plants", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO ods.ods_exposure SELECT dateundergoes, case_status, response_type, session_date FROM african_region_table WHERE dateundergoes > 16\"\n", "labels": {"reads": [{"table": "african_region_table", "columns": ["dateundergoes", "case_status", "response_type", "session_date"]}], "writes": [{"table": "ods.ods_exposure", "columns": ["dateundergoes", "case_status", "response_type", "session_date"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dwd.dwd_risk_score SELECT drug, sale_quantity, preference_score, char_cells FROM micro_mobility WHERE drug > 199;\nEOF\n", "labels": {"reads": [{"table": "micro_mobility", "columns": ["drug", "sale_quantity", "preference_score", "char_cells"]}], "writes": [{"table": "dwd.dwd_risk_score", "columns": ["drug", "sale_quantity", "preference_score", "char_cells"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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": "heldout"}}
{"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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO organisation_types SELECT artist_id, online_dispute_resolution, attraction_name FROM veteranemployees WHERE artist_id > 169\"\n", "labels": {"reads": [{"table": "veteranemployees", "columns": ["artist_id", "online_dispute_resolution", "attraction_name"]}], "writes": [{"table": "organisation_types", "columns": ["artist_id", "online_dispute_resolution", "attraction_name"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = extract_table(ctx, \"dws.dws_exposure_df\")\nsave_to_target(df, \"farm_soil_moisture\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dws.dws_exposure_df", "columns": null}], "writes": [{"table": "farm_soil_moisture", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"social_impact_scores\").where(\"dt = current_date()\")\nevents.writeTo(\"restorative_justice\").append()\n", "labels": {"reads": [{"table": "social_impact_scores", "columns": null}], "writes": [{"table": "restorative_justice", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO elections SELECT is_vegan, royal_family_details, ram_mib, num_sessions FROM community_development.schools WHERE is_vegan > 77\"\n", "labels": {"reads": [{"table": "community_development.schools", "columns": ["is_vegan", "royal_family_details", "ram_mib", "num_sessions"]}], "writes": [{"table": "elections", "columns": ["is_vegan", "royal_family_details", "ram_mib", "num_sessions"]}]}, "meta": {"template_id": "sh-presto", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO language_preservation SELECT discount, veteran_unemployment_rate, candidate_id FROM document_sections WHERE discount > 37\"))\n", "labels": {"reads": [{"table": "document_sections", "columns": ["discount", "veteran_unemployment_rate", "candidate_id"]}], "writes": [{"table": "language_preservation", "columns": ["discount", "veteran_unemployment_rate", "candidate_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"weatherdata\").where(\"dt = current_date()\")\nevents.writeTo(\"content\").append()\n", "labels": {"reads": [{"table": "weatherdata", "columns": null}], "writes": [{"table": "content", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"budgetallocations\", limit=5000)\nresult = value * ratio + offset\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "budgetallocations", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO stg.stg_exposure_df SELECT taskdate, kids, price_per_gram, staff_details FROM ticket_prices WHERE taskdate > 413\"\n", "labels": {"reads": [{"table": "ticket_prices", "columns": ["taskdate", "kids", "price_per_gram", "staff_details"]}], "writes": [{"table": "stg.stg_exposure_df", "columns": ["taskdate", "kids", "price_per_gram", "staff_details"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO furniture_manufacte (bioprocess_id, volunteerdate) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "furniture_manufacte", "columns": ["bioprocess_id", "volunteerdate"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO marine_protected_areas (item_price, post_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "marine_protected_areas", "columns": ["item_price", "post_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"healthcare_workers\").where(\"dt = current_date()\")\nevents.writeTo(\"content\").append()\n", "labels": {"reads": [{"table": "healthcare_workers", "columns": null}], "writes": [{"table": "content", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"sustainable_buildings\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"funds\")\n", "labels": {"reads": [{"table": "sustainable_buildings", "columns": null}], "writes": [{"table": "funds", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"problems\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "problems", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model worker_salaries depends on ods.shipments_daily\ndbt run --select worker_salaries --vars '{\"src\":\"ods.shipments_daily\"}'\n", "labels": {"reads": [{"table": "ods.shipments_daily", "columns": null}], "writes": [{"table": "worker_salaries", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO non_profit_employees SELECT sponsor_name, market_rate, thefttype FROM students_in_detention WHERE sponsor_name > 276\"))\n", "labels": {"reads": [{"table": "students_in_detention", "columns": ["sponsor_name", "market_rate", "thefttype"]}], "writes": [{"table": "non_profit_employees", "columns": ["sponsor_name", "market_rate", "thefttype"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO financial_crimes SELECT book_title, date_test_taken, detention_type_code, primaryaffiliation FROM exoplanets WHERE book_title > 436\"\n", "labels": {"reads": [{"table": "exoplanets", "columns": ["book_title", "date_test_taken", "detention_type_code", "primaryaffiliation"]}], "writes": [{"table": "financial_crimes", "columns": ["book_title", "date_test_taken", "detention_type_code", "primaryaffiliation"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO green_vehicles SELECT * FROM legacy\ncur.execute(\"SELECT team_id_br, ngo_name FROM policyholder LIMIT 249\")\n", "labels": {"reads": [{"table": "policyholder", "columns": ["team_id_br", "ngo_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO sculpture_sales SELECT date_to, practice, wage_increase, postalcode FROM news_ratings WHERE date_to > 433\"\n", "labels": {"reads": [{"table": "news_ratings", "columns": ["date_to", "practice", "wage_increase", "postalcode"]}], "writes": [{"table": "sculpture_sales", "columns": ["date_to", "practice", "wage_increase", "postalcode"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"residential_buildings\").toPandas()\ndf[[\"yearadded\", \"wifi\"]].to_sql(\"member_workout_date\", engine, index=False)\n", "labels": {"reads": [{"table": "residential_buildings", "columns": null}], "writes": [{"table": "member_workout_date", "columns": ["yearadded", "wifi"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"document_drafts\").where(\"dt = current_date()\")\nevents.writeTo(\"artdistribution\").append()\n", "labels": {"reads": [{"table": "document_drafts", "columns": null}], "writes": [{"table": "artdistribution", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model sculpture_sales depends on satellite_missions\ndbt run --select sculpture_sales --vars '{\"src\":\"satellite_missions\"}'\n", "labels": {"reads": [{"table": "satellite_missions", "columns": null}], "writes": [{"table": "sculpture_sales", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO ods_vendors_di SELECT a.iata, b.unitsperweek FROM country_production a JOIN studentsmentalhealth b ON a.weeks_on_top = b.weeks_on_top\"\n", "labels": {"reads": [{"table": "country_production", "columns": null}, {"table": "studentsmentalhealth", "columns": null}], "writes": [{"table": "ods_vendors_di", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM salaries\"\n", "labels": {"reads": [{"table": "salaries", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nsql = \"INSERT INTO instruments SELECT a.founder, b.complaint_id FROM volunteer_programs a JOIN fairtradeorders b ON a.stuid = b.stuid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "volunteer_programs", "columns": null}, {"table": "fairtradeorders", "columns": null}], "writes": [{"table": "instruments", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM regionwildlifehabitats\"\n", "labels": {"reads": [{"table": "regionwildlifehabitats", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"trained_in\", limit=5000)\nimport logging\n", "labels": {"reads": [{"table": "trained_in", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO payment SELECT software_platform, form_type_code, faculty_id, zip_postcode FROM treatment_type WHERE software_platform > 332\"\n", "labels": {"reads": [{"table": "treatment_type", "columns": ["software_platform", "form_type_code", "faculty_id", "zip_postcode"]}], "writes": [{"table": "payment", "columns": ["software_platform", "form_type_code", "faculty_id", "zip_postcode"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"genres\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nlogger = logging.getLogger(__name__)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "genres", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model donationdates depends on ads.exposure_daily\ndbt run --models donationdates --vars '{\"source_table\":\"ads.exposure_daily\"}'\n", "labels": {"reads": [{"table": "ads.exposure_daily", "columns": null}], "writes": [{"table": "donationdates", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO spaceships SELECT a.product_size, b.averagespeed FROM citydata a JOIN cyber_attacks b ON a.parent_organization_id = b.parent_organization_id\"\n", "labels": {"reads": [{"table": "citydata", "columns": null}, {"table": "cyber_attacks", "columns": null}], "writes": [{"table": "spaceships", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM bi.bi_orders\", conn)\ndf.to_sql(\"supplier_products\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "bi.bi_orders", "columns": null}], "writes": [{"table": "supplier_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": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"screenings\", writes=\"broadcast\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "screenings", "columns": null}], "writes": [{"table": "broadcast", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO services SELECT releasedate, volunteerjoindate, practicename FROM landfillcapacity WHERE releasedate > 283\"))\n", "labels": {"reads": [{"table": "landfillcapacity", "columns": ["releasedate", "volunteerjoindate", "practicename"]}], "writes": [{"table": "services", "columns": ["releasedate", "volunteerjoindate", "practicename"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO ocean_acidity_records SELECT dept_store_chain_id, donation_amount, stu_phone, experienceid FROM member_workout_date WHERE dept_store_chain_id > 192\"\n", "labels": {"reads": [{"table": "member_workout_date", "columns": ["dept_store_chain_id", "donation_amount", "stu_phone", "experienceid"]}], "writes": [{"table": "ocean_acidity_records", "columns": ["dept_store_chain_id", "donation_amount", "stu_phone", "experienceid"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO playlists (course_name, production_value) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "playlists", "columns": ["course_name", "production_value"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"studios\", limit=5000)\nimport logging\n", "labels": {"reads": [{"table": "studios", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO payment SELECT initiative_id, virtual_tour_views, algorithm_name, to_address FROM mart.mart_risk_score_df WHERE initiative_id > 176\"\n", "labels": {"reads": [{"table": "mart.mart_risk_score_df", "columns": ["initiative_id", "virtual_tour_views", "algorithm_name", "to_address"]}], "writes": [{"table": "payment", "columns": ["initiative_id", "virtual_tour_views", "algorithm_name", "to_address"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO privacy_settings SELECT a.premise_id, b.fault_log_entry_id FROM dws.events_daily a JOIN job_history b ON a.exploited = b.exploited\"\n", "labels": {"reads": [{"table": "dws.events_daily", "columns": null}, {"table": "job_history", "columns": null}], "writes": [{"table": "privacy_settings", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO transactions_lots SELECT deaths, station_name, institution_name FROM elections WHERE deaths > 87\"\n", "labels": {"reads": [{"table": "elections", "columns": ["deaths", "station_name", "institution_name"]}], "writes": [{"table": "transactions_lots", "columns": ["deaths", "station_name", "institution_name"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model marketing depends on funding\ndbt run --select marketing --vars '{\"src\":\"funding\"}'\n", "labels": {"reads": [{"table": "funding", "columns": null}], "writes": [{"table": "marketing", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO fairtradeorders SELECT section_title, lesson_time FROM documents_with_expenses WHERE section_title > 172\"))\n", "labels": {"reads": [{"table": "documents_with_expenses", "columns": ["section_title", "lesson_time"]}], "writes": [{"table": "fairtradeorders", "columns": ["section_title", "lesson_time"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"vesseldocking\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"georgia_rural_residents\")\n", "labels": {"reads": [{"table": "vesseldocking", "columns": null}], "writes": [{"table": "georgia_rural_residents", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO students_in_detention SELECT working_year_starts, semester, spent FROM ods.shipments_daily WHERE working_year_starts > 5\"\n", "labels": {"reads": [{"table": "ods.shipments_daily", "columns": ["working_year_starts", "semester", "spent"]}], "writes": [{"table": "students_in_detention", "columns": ["working_year_starts", "semester", "spent"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"europe_org\").select([\"id\", \"amt\"]).copy_into(\"mill\").commit()\n", "labels": {"reads": [{"table": "europe_org", "columns": null}], "writes": [{"table": "mill", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO dw.campaigns SELECT race, sea, cityname FROM dws.dws_refunds_full WHERE race > 127\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_full", "columns": ["race", "sea", "cityname"]}], "writes": [{"table": "dw.campaigns", "columns": ["race", "sea", "cityname"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"member_workouts\", writes=\"client_transactions\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "member_workouts", "columns": null}], "writes": [{"table": "client_transactions", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO cargo SELECT feature_details, amount_settled, update_date, phone_number FROM vesseldocking WHERE feature_details > 145\"\n", "labels": {"reads": [{"table": "vesseldocking", "columns": ["feature_details", "amount_settled", "update_date", "phone_number"]}], "writes": [{"table": "cargo", "columns": ["feature_details", "amount_settled", "update_date", "phone_number"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"virtual_tours_usa\", limit=5000)\nimport logging\n", "labels": {"reads": [{"table": "virtual_tours_usa", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"dws.dws_cart_item_di\", writes=\"medical_staff\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dws.dws_cart_item_di", "columns": null}], "writes": [{"table": "medical_staff", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"concertticketsalesbycountry\").select([\"id\", \"amt\"]).copy_into(\"client\").commit()\n", "labels": {"reads": [{"table": "concertticketsalesbycountry", "columns": null}], "writes": [{"table": "client", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"ods.shipments_daily\").where(\"dt = current_date()\")\nevents.writeTo(\"mart.sessions_hourly\").append()\n", "labels": {"reads": [{"table": "ods.shipments_daily", "columns": null}], "writes": [{"table": "mart.sessions_hourly", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO dws.dws_vendors_daily SELECT has_spf, product_color FROM sector WHERE has_spf > 226\"\n", "labels": {"reads": [{"table": "sector", "columns": ["has_spf", "product_color"]}], "writes": [{"table": "dws.dws_vendors_daily", "columns": ["has_spf", "product_color"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM student_course_enrolment\", conn)\ndf.to_sql(\"autonomous_taxis\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "student_course_enrolment", "columns": null}], "writes": [{"table": "autonomous_taxis", "columns": null}]}, "meta": {"template_id": "py-pandas-sql", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"graduatestudents\").select([\"id\", \"amt\"]).copy_into(\"bi.cart_item\").commit()\n", "labels": {"reads": [{"table": "graduatestudents", "columns": null}], "writes": [{"table": "bi.cart_item", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"public_buildings\").where(\"dt = current_date()\")\nevents.writeTo(\"delivery_routes\").append()\n", "labels": {"reads": [{"table": "public_buildings", "columns": null}], "writes": [{"table": "delivery_routes", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO dws.dws_inventory_full SELECT uid, claim_date FROM bi_vendors_daily WHERE uid > 497\"\n", "labels": {"reads": [{"table": "bi_vendors_daily", "columns": ["uid", "claim_date"]}], "writes": [{"table": "dws.dws_inventory_full", "columns": ["uid", "claim_date"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO gcc_shariah_financing SELECT schedule_id, astronautid, shale_play, emp_num FROM documents_mailed WHERE schedule_id > 127\"))\n", "labels": {"reads": [{"table": "documents_mailed", "columns": ["schedule_id", "astronautid", "shale_play", "emp_num"]}], "writes": [{"table": "gcc_shariah_financing", "columns": ["schedule_id", "astronautid", "shale_play", "emp_num"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO investigative_journalism SELECT * FROM legacy\ncur.execute(\"SELECT registration_id, max_speed FROM peacekeeping_operations LIMIT 494\")\n", "labels": {"reads": [{"table": "peacekeeping_operations", "columns": ["registration_id", "max_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": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"claim_headers\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "claim_headers", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model claim_headers depends on client_transactions\ndbt run --select claim_headers --vars '{\"src\":\"client_transactions\"}'\n", "labels": {"reads": [{"table": "client_transactions", "columns": null}], "writes": [{"table": "claim_headers", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"mill\").select([\"id\", \"amt\"]).copy_into(\"threat_actors\").commit()\n", "labels": {"reads": [{"table": "mill", "columns": null}], "writes": [{"table": "threat_actors", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model bi_clicks_daily depends on payment\ndbt run --select bi_clicks_daily --vars '{\"src\":\"payment\"}'\n", "labels": {"reads": [{"table": "payment", "columns": null}], "writes": [{"table": "bi_clicks_daily", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO suppliers SELECT prepnurse, hiredate, i_id FROM vendor WHERE prepnurse > 269\"\n", "labels": {"reads": [{"table": "vendor", "columns": ["prepnurse", "hiredate", "i_id"]}], "writes": [{"table": "suppliers", "columns": ["prepnurse", "hiredate", "i_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO claim_headers SELECT product_details, competition_type FROM client WHERE product_details > 219\"))\n", "labels": {"reads": [{"table": "client", "columns": ["product_details", "competition_type"]}], "writes": [{"table": "claim_headers", "columns": ["product_details", "competition_type"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"urban_agriculture\", writes=\"storm\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "urban_agriculture", "columns": null}], "writes": [{"table": "storm", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model tech_companies depends on concertinfo\ndbt run --select tech_companies --vars '{\"src\":\"concertinfo\"}'\n", "labels": {"reads": [{"table": "concertinfo", "columns": null}], "writes": [{"table": "tech_companies", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"studentsmentalhealth\").select([\"id\", \"amt\"]).copy_into(\"ruralinfrastructure\").commit()\n", "labels": {"reads": [{"table": "studentsmentalhealth", "columns": null}], "writes": [{"table": "ruralinfrastructure", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"classes\", writes=\"rural_health_centers\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "classes", "columns": null}], "writes": [{"table": "rural_health_centers", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO sustainable_tourism SELECT street_address, grant_amount FROM mart.mart_cart_item_daily WHERE street_address > 35\"\n", "labels": {"reads": [{"table": "mart.mart_cart_item_daily", "columns": ["street_address", "grant_amount"]}], "writes": [{"table": "sustainable_tourism", "columns": ["street_address", "grant_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": "heldout"}}
{"task_type": "SHELL", "content": "# model airports depends on smartcityinitiatives\ndbt run --select airports --vars '{\"src\":\"smartcityinitiatives\"}'\n", "labels": {"reads": [{"table": "smartcityinitiatives", "columns": null}], "writes": [{"table": "airports", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT ei_value, enable_third_party_ads FROM member_workout_date LIMIT 211;\nINSERT INTO fairness_reports SELECT sodium, sportname, audienceid FROM ferry_routes WHERE sodium > 317;\nSQL\n", "labels": {"reads": [{"table": "member_workout_date", "columns": ["ei_value", "enable_third_party_ads"]}, {"table": "ferry_routes", "columns": ["sodium", "sportname", "audienceid"]}], "writes": [{"table": "fairness_reports", "columns": ["sodium", "sportname", "audienceid"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model nonprofits depends on addresses\ndbt run --select nonprofits --vars '{\"src\":\"addresses\"}'\n", "labels": {"reads": [{"table": "addresses", "columns": null}], "writes": [{"table": "nonprofits", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO member_workout_date SELECT lipstick_id, postal_code, environmental_impact FROM dws.products_daily WHERE lipstick_id > 32\"\n", "labels": {"reads": [{"table": "dws.products_daily", "columns": ["lipstick_id", "postal_code", "environmental_impact"]}], "writes": [{"table": "member_workout_date", "columns": ["lipstick_id", "postal_code", "environmental_impact"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws.dws_inventory_full\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"public_buses\")\n", "labels": {"reads": [{"table": "dws.dws_inventory_full", "columns": null}], "writes": [{"table": "public_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": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO workout_records 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": "heldout"}}
{"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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO dw.dw_campaigns_df SELECT manager_id, actual_order_id FROM marine_protected_areas WHERE manager_id > 41\"\n", "labels": {"reads": [{"table": "marine_protected_areas", "columns": ["manager_id", "actual_order_id"]}], "writes": [{"table": "dw.dw_campaigns_df", "columns": ["manager_id", "actual_order_id"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ocean_temperature\", writes=\"claim\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ocean_temperature", "columns": null}], "writes": [{"table": "claim", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO fleet_vessels SELECT rural_area, hourdate, department, brand_mentioned FROM therapeutic_areas WHERE rural_area > 240\"))\n", "labels": {"reads": [{"table": "therapeutic_areas", "columns": ["rural_area", "hourdate", "department", "brand_mentioned"]}], "writes": [{"table": "fleet_vessels", "columns": ["rural_area", "hourdate", "department", "brand_mentioned"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model member_workout_date depends on volunteer_programs\ndbt run --select member_workout_date --vars '{\"src\":\"volunteer_programs\"}'\n", "labels": {"reads": [{"table": "volunteer_programs", "columns": null}], "writes": [{"table": "member_workout_date", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ads.ads_campaigns_delta\", writes=\"organisation_types\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ads.ads_campaigns_delta", "columns": null}], "writes": [{"table": "organisation_types", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ads_exposure_daily\", limit=5000)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "ads_exposure_daily", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO playlists SELECT number_of_hosts, category, transaction_date FROM schedule WHERE number_of_hosts > 264\"\n", "labels": {"reads": [{"table": "schedule", "columns": ["number_of_hosts", "category", "transaction_date"]}], "writes": [{"table": "playlists", "columns": ["number_of_hosts", "category", "transaction_date"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"sustainable_buildings\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "sustainable_buildings", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT customer_number, artwork_id FROM lifelong_learning LIMIT 214\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "lifelong_learning", "columns": ["customer_number", "artwork_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": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO vesseldocking 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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO language_preservation SELECT technique, other_item_details, date_of_transaction FROM mlb_teams_mascots WHERE technique > 130\"\n", "labels": {"reads": [{"table": "mlb_teams_mascots", "columns": ["technique", "other_item_details", "date_of_transaction"]}], "writes": [{"table": "language_preservation", "columns": ["technique", "other_item_details", "date_of_transaction"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO actor SELECT stu_dob, entryid FROM ods.ods_refunds_delta WHERE stu_dob > 473\"\n", "labels": {"reads": [{"table": "ods.ods_refunds_delta", "columns": ["stu_dob", "entryid"]}], "writes": [{"table": "actor", "columns": ["stu_dob", "entryid"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ads.ads_coupon_use\", limit=5000)\nmetrics.append(round(score, 4))\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "ads.ads_coupon_use", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"documents_with_expenses\", writes=\"elements\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "documents_with_expenses", "columns": null}], "writes": [{"table": "elements", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO problems 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": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT day_number, ll_id FROM workforce_diversity\", engine)\nimport logging\nmetrics.append(round(score, 4))\ndf.to_sql(\"claim_headers\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "workforce_diversity", "columns": ["day_number", "ll_id"]}], "writes": [{"table": "claim_headers", "columns": null}]}, "meta": {"template_id": "py-pandas-roundtrip", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "result = value * ratio + offset\nsql = \"INSERT INTO dw.payments_full SELECT a.acc_regular_season, b.carriername FROM product_ingredients a JOIN production_quebec b ON a.grant_type = b.grant_type\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "product_ingredients", "columns": null}, {"table": "production_quebec", "columns": null}], "writes": [{"table": "dw.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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"payment\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"intelligencebudget\")\n", "labels": {"reads": [{"table": "payment", "columns": null}], "writes": [{"table": "intelligencebudget", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO client_transactions SELECT products_this_year, winning_aircraft, produceid, build_year FROM healthcare_providers WHERE products_this_year > 250\"\n", "labels": {"reads": [{"table": "healthcare_providers", "columns": ["products_this_year", "winning_aircraft", "produceid", "build_year"]}], "writes": [{"table": "client_transactions", "columns": ["products_this_year", "winning_aircraft", "produceid", "build_year"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO farmwatertemp SELECT wellid, temporary_acting FROM vesseldocking WHERE wellid > 358\"\n", "labels": {"reads": [{"table": "vesseldocking", "columns": ["wellid", "temporary_acting"]}], "writes": [{"table": "farmwatertemp", "columns": ["wellid", "temporary_acting"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model deliveries depends on cyber_attacks\ndbt run -s deliveries --vars '{\"source_table\":\"cyber_attacks\"}'\n", "labels": {"reads": [{"table": "cyber_attacks", "columns": null}], "writes": [{"table": "deliveries", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT wastetype, passenger_count FROM union_finance LIMIT 267;\nINSERT INTO aircraft_and_flight_hours SELECT mappinglength, founder FROM mental_health_providers WHERE mappinglength > 145;\nSQL\n", "labels": {"reads": [{"table": "union_finance", "columns": ["wastetype", "passenger_count"]}, {"table": "mental_health_providers", "columns": ["mappinglength", "founder"]}], "writes": [{"table": "aircraft_and_flight_hours", "columns": ["mappinglength", "founder"]}]}, "meta": {"template_id": "sh-heredoc", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"mental_health_facilities\").select([\"id\", \"amt\"]).copy_into(\"biotechstartupfunding\").commit()\n", "labels": {"reads": [{"table": "mental_health_facilities", "columns": null}], "writes": [{"table": "biotechstartupfunding", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"statements\", limit=5000)\nimport logging\nmetrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "statements", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model intangible_heritage depends on esports_events\ndbt build --models intangible_heritage --vars '{\"src\":\"esports_events\"}'\n", "labels": {"reads": [{"table": "esports_events", "columns": null}], "writes": [{"table": "intangible_heritage", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table tourists --target-dir /tmp/land\n", "labels": {"reads": [{"table": "tourists", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO problems SELECT date_of_publication, built, number_of_vessels, co_id FROM public_buildings WHERE date_of_publication > 106\"))\n", "labels": {"reads": [{"table": "public_buildings", "columns": ["date_of_publication", "built", "number_of_vessels", "co_id"]}], "writes": [{"table": "problems", "columns": ["date_of_publication", "built", "number_of_vessels", "co_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model violation depends on incident_regions\ndbt run --select violation --vars '{\"src\":\"incident_regions\"}'\n", "labels": {"reads": [{"table": "incident_regions", "columns": null}], "writes": [{"table": "violation", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"national_security_agencies\", limit=5000)\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\n", "labels": {"reads": [{"table": "national_security_agencies", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "set -euo pipefail\nhive -e \"INSERT INTO oregondispensaries SELECT teamid, problem_id FROM games WHERE teamid > 295\"\n", "labels": {"reads": [{"table": "games", "columns": ["teamid", "problem_id"]}], "writes": [{"table": "oregondispensaries", "columns": ["teamid", "problem_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO union_stats SELECT year_join, court_id, closure_authorised_by_staff_id FROM dws.dws_inventory_full WHERE year_join > 8\"\n", "labels": {"reads": [{"table": "dws.dws_inventory_full", "columns": ["year_join", "court_id", "closure_authorised_by_staff_id"]}], "writes": [{"table": "union_stats", "columns": ["year_join", "court_id", "closure_authorised_by_staff_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table stg_exposure_delta --columns is_valid,incident_type_description --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "stg_exposure_delta", "columns": ["is_valid", "incident_type_description"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO dwd.device_log_daily (phase, recruitername) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "dwd.device_log_daily", "columns": ["phase", "recruitername"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model social_impact_scores depends on visitor_demographics\ndbt run --select social_impact_scores --vars '{\"src\":\"visitor_demographics\"}'\n", "labels": {"reads": [{"table": "visitor_demographics", "columns": null}], "writes": [{"table": "social_impact_scores", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO supplier_products SELECT * FROM legacy\ncur.execute(\"SELECT bedroom_count, resolution FROM marine_protected_areas LIMIT 38\")\n", "labels": {"reads": [{"table": "marine_protected_areas", "columns": ["bedroom_count", "resolution"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT individual_id, open_year FROM visitor_demographics LIMIT 58\")\nimport logging\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO trend_popularity SELECT source_u_id, supply_volume, allocation_date, platform_id FROM personfriend WHERE source_u_id > 288\")\n", "labels": {"reads": [{"table": "visitor_demographics", "columns": ["individual_id", "open_year"]}, {"table": "personfriend", "columns": ["source_u_id", "supply_volume", "allocation_date", "platform_id"]}], "writes": [{"table": "trend_popularity", "columns": ["source_u_id", "supply_volume", "allocation_date", "platform_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"ships\").where(\"dt = current_date()\")\nevents.writeTo(\"dws.clicks_hourly\").append()\n", "labels": {"reads": [{"table": "ships", "columns": null}], "writes": [{"table": "dws.clicks_hourly", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO hospital_beds SELECT service_details, register_year FROM incident_regions WHERE service_details > 37\"\n", "labels": {"reads": [{"table": "incident_regions", "columns": ["service_details", "register_year"]}], "writes": [{"table": "hospital_beds", "columns": ["service_details", "register_year"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"elements\").select([\"id\", \"amt\"]).copy_into(\"ai_recs\").commit()\n", "labels": {"reads": [{"table": "elements", "columns": null}], "writes": [{"table": "ai_recs", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO jobopenings SELECT donation_date, brand_id, eventtype FROM digital_divide WHERE donation_date > 105\"))\n", "labels": {"reads": [{"table": "digital_divide", "columns": ["donation_date", "brand_id", "eventtype"]}], "writes": [{"table": "jobopenings", "columns": ["donation_date", "brand_id", "eventtype"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dwd.dwd_refunds_hourly\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"safetyrecord\")\n", "labels": {"reads": [{"table": "dwd.dwd_refunds_hourly", "columns": null}], "writes": [{"table": "safetyrecord", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO dws.dws_products_di SELECT * FROM legacy\ncur.execute(\"SELECT sustainable_practice, business_size FROM stg_sessions_daily LIMIT 147\")\n", "labels": {"reads": [{"table": "stg_sessions_daily", "columns": ["sustainable_practice", "business_size"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"claim_headers\", writes=\"retail\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "claim_headers", "columns": null}], "writes": [{"table": "retail", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO gas_production SELECT workeridentity, eco_friendly FROM ads.ads_member_point_delta WHERE workeridentity > 172\"\n", "labels": {"reads": [{"table": "ads.ads_member_point_delta", "columns": ["workeridentity", "eco_friendly"]}], "writes": [{"table": "gas_production", "columns": ["workeridentity", "eco_friendly"]}]}, "meta": {"template_id": "sh-beeline", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"nonprofits\", limit=5000)\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "nonprofits", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO storm SELECT shelter_name, total_cost, longitude, creator FROM documents_with_expenses WHERE shelter_name > 428\"\n", "labels": {"reads": [{"table": "documents_with_expenses", "columns": ["shelter_name", "total_cost", "longitude", "creator"]}], "writes": [{"table": "storm", "columns": ["shelter_name", "total_cost", "longitude", "creator"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO taj_mahal_info SELECT major, purchase_details, energy_efficiency_savings, granteeid FROM skincare_ingredients WHERE major > 434\"\n", "labels": {"reads": [{"table": "skincare_ingredients", "columns": ["major", "purchase_details", "energy_efficiency_savings", "granteeid"]}], "writes": [{"table": "taj_mahal_info", "columns": ["major", "purchase_details", "energy_efficiency_savings", "granteeid"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = source_dataset(ctx, \"launches\")\nexport_to_warehouse(df, \"models\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "launches", "columns": null}], "writes": [{"table": "models", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO dw.campaigns SELECT coverage_type, worker_id, orgid, developer FROM heritage_tours WHERE coverage_type > 358\"))\n", "labels": {"reads": [{"table": "heritage_tours", "columns": ["coverage_type", "worker_id", "orgid", "developer"]}], "writes": [{"table": "dw.campaigns", "columns": ["coverage_type", "worker_id", "orgid", "developer"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"games\").where(\"dt = current_date()\")\nevents.writeTo(\"ads.exposure_daily\").append()\n", "labels": {"reads": [{"table": "games", "columns": null}], "writes": [{"table": "ads.exposure_daily", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"document_drafts\").select([\"id\", \"amt\"]).copy_into(\"ads.exposure_daily\").commit()\n", "labels": {"reads": [{"table": "document_drafts", "columns": null}], "writes": [{"table": "ads.exposure_daily", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO ocean_trenches SELECT goals, customer_address, meal_name FROM job_history WHERE goals > 81\"\n", "labels": {"reads": [{"table": "job_history", "columns": ["goals", "customer_address", "meal_name"]}], "writes": [{"table": "ocean_trenches", "columns": ["goals", "customer_address", "meal_name"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model coastal_erosion depends on elections\ndbt run --select coastal_erosion --vars '{\"src\":\"elections\"}'\n", "labels": {"reads": [{"table": "elections", "columns": null}], "writes": [{"table": "coastal_erosion", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"cargo\").where(\"dt = current_date()\")\nevents.writeTo(\"production_quebec\").append()\n", "labels": {"reads": [{"table": "cargo", "columns": null}], "writes": [{"table": "production_quebec", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO dysprosium_production SELECT watertemp, order_item_id FROM farm_soil_moisture WHERE watertemp > 156\"], check=True)\n", "labels": {"reads": [{"table": "farm_soil_moisture", "columns": ["watertemp", "order_item_id"]}], "writes": [{"table": "dysprosium_production", "columns": ["watertemp", "order_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = pull_input(ctx, \"techniques\")\npersist_to_store(df, \"member_workouts\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "techniques", "columns": null}], "writes": [{"table": "member_workouts", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"dw.payments_full\").select([\"id\", \"amt\"]).copy_into(\"lanthanummines\").commit()\n", "labels": {"reads": [{"table": "dw.payments_full", "columns": null}], "writes": [{"table": "lanthanummines", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"artdistribution\", writes=\"inspections_tx\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "artdistribution", "columns": null}], "writes": [{"table": "inspections_tx", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql \"$DB_URL\" <<SQL\nSELECT player_id, permitid FROM tourism_impact LIMIT 435;\nINSERT INTO mental_health_facilities SELECT chromosome, amount_funded, contract_start_date FROM ods.ods_member_point WHERE chromosome > 396;\nSQL\n", "labels": {"reads": [{"table": "tourism_impact", "columns": ["player_id", "permitid"]}, {"table": "ods.ods_member_point", "columns": ["chromosome", "amount_funded", "contract_start_date"]}], "writes": [{"table": "mental_health_facilities", "columns": ["chromosome", "amount_funded", "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": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO vehicle_safety SELECT time_of_purchase, asset_disposed_date FROM artifactcounts WHERE time_of_purchase > 435;\nEOF\n", "labels": {"reads": [{"table": "artifactcounts", "columns": ["time_of_purchase", "asset_disposed_date"]}], "writes": [{"table": "vehicle_safety", "columns": ["time_of_purchase", "asset_disposed_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": "heldout"}}
{"task_type": "SHELL", "content": "# model documents_with_expenses depends on game\ndbt run --select documents_with_expenses --vars '{\"src\":\"game\"}'\n", "labels": {"reads": [{"table": "game", "columns": null}], "writes": [{"table": "documents_with_expenses", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model sculpture_sales depends on documents_with_expenses\ndbt run --select sculpture_sales --vars '{\"src\":\"documents_with_expenses\"}'\n", "labels": {"reads": [{"table": "documents_with_expenses", "columns": null}], "writes": [{"table": "sculpture_sales", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"water_treatment_plant_upgrades\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "water_treatment_plant_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO moviebudgets SELECT played, workshop_name, dept_store_chain_id FROM dw.dw_refunds_delta WHERE played > 176\"\n", "labels": {"reads": [{"table": "dw.dw_refunds_delta", "columns": ["played", "workshop_name", "dept_store_chain_id"]}], "writes": [{"table": "moviebudgets", "columns": ["played", "workshop_name", "dept_store_chain_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO baseball_players SELECT length_feet, dept_code, balance FROM techniques WHERE length_feet > 420\"\n", "labels": {"reads": [{"table": "techniques", "columns": ["length_feet", "dept_code", "balance"]}], "writes": [{"table": "baseball_players", "columns": ["length_feet", "dept_code", "balance"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO london_buses SELECT meter_200, total_passengers, fundingagency FROM rural_health_centers WHERE meter_200 > 21\"\n", "labels": {"reads": [{"table": "rural_health_centers", "columns": ["meter_200", "total_passengers", "fundingagency"]}], "writes": [{"table": "london_buses", "columns": ["meter_200", "total_passengers", "fundingagency"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO vehicle_safety SELECT credit_score, platform, updatedate FROM digital_divide WHERE credit_score > 464\"))\n", "labels": {"reads": [{"table": "digital_divide", "columns": ["credit_score", "platform", "updatedate"]}], "writes": [{"table": "vehicle_safety", "columns": ["credit_score", "platform", "updatedate"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"countryincome\").where(\"dt = current_date()\")\nevents.writeTo(\"freight_forwarding\").append()\n", "labels": {"reads": [{"table": "countryincome", "columns": null}], "writes": [{"table": "freight_forwarding", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO humanitarianassistance SELECT individual_name, revenueid, checkout, hours FROM budgetallocation WHERE individual_name > 463\"\n", "labels": {"reads": [{"table": "budgetallocation", "columns": ["individual_name", "revenueid", "checkout", "hours"]}], "writes": [{"table": "humanitarianassistance", "columns": ["individual_name", "revenueid", "checkout", "hours"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT share_count, show_id FROM shared_bicycles\", engine)\nimport logging\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"union_membership_statistics\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "shared_bicycles", "columns": ["share_count", "show_id"]}], "writes": [{"table": "union_membership_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": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"products_for_hire\", writes=\"gold_mines\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "products_for_hire", "columns": null}], "writes": [{"table": "gold_mines", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ingredient_sources\", limit=5000)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ingredient_sources", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO mining_company_revenue SELECT is_vegetarian, effort_id, ironid, truck_licence_number FROM document_sections WHERE is_vegetarian > 297\"))\n", "labels": {"reads": [{"table": "document_sections", "columns": ["is_vegetarian", "effort_id", "ironid", "truck_licence_number"]}], "writes": [{"table": "mining_company_revenue", "columns": ["is_vegetarian", "effort_id", "ironid", "truck_licence_number"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dwd_shipments_full\", limit=5000)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [{"table": "dwd_shipments_full", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model research_outcomes depends on students_in_detention\ndbt run --select research_outcomes --vars '{\"src\":\"students_in_detention\"}'\n", "labels": {"reads": [{"table": "students_in_detention", "columns": null}], "writes": [{"table": "research_outcomes", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"mascaras\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nimport logging\n", "labels": {"reads": [{"table": "mascaras", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model lawprograms depends on vehicle_speed\ndbt run --select lawprograms --vars '{\"src\":\"vehicle_speed\"}'\n", "labels": {"reads": [{"table": "vehicle_speed", "columns": null}], "writes": [{"table": "lawprograms", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"concertinfo\", writes=\"ads.ads_campaigns_delta\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "concertinfo", "columns": null}], "writes": [{"table": "ads.ads_campaigns_delta", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"broadcast\").toPandas()\ndf[[\"points_per_game\", \"water_temp\"]].to_sql(\"stg_exposure_delta\", engine, index=False)\n", "labels": {"reads": [{"table": "broadcast", "columns": null}], "writes": [{"table": "stg_exposure_delta", "columns": ["points_per_game", "water_temp"]}]}, "meta": {"template_id": "py-to-sql-columns", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO medication (total_horses, ai_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "medication", "columns": ["total_horses", "ai_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": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT employee, actor_name FROM sustainable_urbanism LIMIT 17\")\nimport logging\nspark.sql(\"INSERT INTO threat_actors SELECT birth_country, lastclaimdate, vessel FROM facilities WHERE birth_country > 480\")\n", "labels": {"reads": [{"table": "sustainable_urbanism", "columns": ["employee", "actor_name"]}, {"table": "facilities", "columns": ["birth_country", "lastclaimdate", "vessel"]}], "writes": [{"table": "threat_actors", "columns": ["birth_country", "lastclaimdate", "vessel"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO hotel_ai SELECT hiv, marketing_region_descriptrion FROM stg.stg_refunds WHERE hiv > 53\"\n", "labels": {"reads": [{"table": "stg.stg_refunds", "columns": ["hiv", "marketing_region_descriptrion"]}], "writes": [{"table": "hotel_ai", "columns": ["hiv", "marketing_region_descriptrion"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model billing depends on satellite_launches\ndbt run --select billing --vars '{\"source_table\":\"satellite_launches\"}'\n", "labels": {"reads": [{"table": "satellite_launches", "columns": null}], "writes": [{"table": "billing", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"cyber_attacks\", writes=\"medication\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "cyber_attacks", "columns": null}], "writes": [{"table": "medication", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"sales.games\").where(\"dt = current_date()\")\nevents.writeTo(\"projects_pakistan\").append()\n", "labels": {"reads": [{"table": "sales.games", "columns": null}], "writes": [{"table": "projects_pakistan", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"document_sections\").where(\"dt = current_date()\")\nevents.writeTo(\"renewable_energy\").append()\n", "labels": {"reads": [{"table": "document_sections", "columns": null}], "writes": [{"table": "renewable_energy", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO social_impact_scores SELECT servicename, incident_count, fuelid FROM sustainable_urbanism WHERE servicename > 59;\nEOF\n", "labels": {"reads": [{"table": "sustainable_urbanism", "columns": ["servicename", "incident_count", "fuelid"]}], "writes": [{"table": "social_impact_scores", "columns": ["servicename", "incident_count", "fuelid"]}]}, "meta": {"template_id": "sh-sqlplus", "rule_covered": true, "form_family": "cli", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO users SELECT employeename, budget_in_billions FROM ferry_routes WHERE employeename > 124\"\n", "labels": {"reads": [{"table": "ferry_routes", "columns": ["employeename", "budget_in_billions"]}], "writes": [{"table": "users", "columns": ["employeename", "budget_in_billions"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"techniques\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "techniques", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO regionwildlifehabitats 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": "heldout"}}
{"task_type": "SHELL", "content": "# model habitat1 depends on ocean_temperature\ndbt build --select habitat1 --vars 'source: ocean_temperature'\n", "labels": {"reads": [{"table": "ocean_temperature", "columns": null}], "writes": [{"table": "habitat1", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"regionwildlifehabitats\").select([\"id\", \"amt\"]).copy_into(\"musicevents\").commit()\n", "labels": {"reads": [{"table": "regionwildlifehabitats", "columns": null}], "writes": [{"table": "musicevents", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "trap 'echo failed' ERR\nhive -e \"INSERT INTO ticket_prices SELECT avg_yield, dept_code, cb_year, decision FROM intangible_heritage WHERE avg_yield > 371\"\n", "labels": {"reads": [{"table": "intangible_heritage", "columns": ["avg_yield", "dept_code", "cb_year", "decision"]}], "writes": [{"table": "ticket_prices", "columns": ["avg_yield", "dept_code", "cb_year", "decision"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT founder_lgbtq, movie_id FROM lifelong_learning\", engine)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\ndf.to_sql(\"media_content\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "lifelong_learning", "columns": ["founder_lgbtq", "movie_id"]}], "writes": [{"table": "media_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": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.license_plate > 5).all()\n# src table: screenings\nengine.execute(\"INSERT INTO dw.dw_vendors_hourly SELECT * FROM screenings\")\n", "labels": {"reads": [{"table": "screenings", "columns": null}], "writes": [{"table": "dw.dw_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": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.individual_middle_name > 213).all()\n# src table: physician\nengine.execute(\"INSERT INTO mart.mart_device_log_df SELECT * FROM physician\")\n", "labels": {"reads": [{"table": "physician", "columns": null}], "writes": [{"table": "mart.mart_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": "heldout"}}
{"task_type": "SHELL", "content": "# model ods_vendors_di depends on healthcare_access\ndbt run --select ods_vendors_di --vars '{\"src\":\"healthcare_access\"}'\n", "labels": {"reads": [{"table": "healthcare_access", "columns": null}], "writes": [{"table": "ods_vendors_di", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ods.ods_exposure\", limit=5000)\nresult = value * ratio + offset\nif not rows:\n    logger.warning('empty result')\nimport logging\n", "labels": {"reads": [{"table": "ods.ods_exposure", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"organisation_types\").where(\"dt = current_date()\")\nevents.writeTo(\"financial_institutions\").append()\n", "labels": {"reads": [{"table": "organisation_types", "columns": null}], "writes": [{"table": "financial_institutions", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO event_types SELECT production, sanctuary FROM country WHERE production > 157\"\n", "labels": {"reads": [{"table": "country", "columns": ["production", "sanctuary"]}], "writes": [{"table": "event_types", "columns": ["production", "sanctuary"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO ethical_ai_courses_year SELECT min_temperature_f, plan_id, document_status_description, num_sustainable_materials FROM donationdates WHERE min_temperature_f > 54\"\n", "labels": {"reads": [{"table": "donationdates", "columns": ["min_temperature_f", "plan_id", "document_status_description", "num_sustainable_materials"]}], "writes": [{"table": "ethical_ai_courses_year", "columns": ["min_temperature_f", "plan_id", "document_status_description", "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": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table public_buses --columns dept_name,vr_platform --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "public_buses", "columns": ["dept_name", "vr_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": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO ai_researcher SELECT funding_round_id, dst_apid FROM production_quebec WHERE funding_round_id > 55\"\n", "labels": {"reads": [{"table": "production_quebec", "columns": ["funding_round_id", "dst_apid"]}], "writes": [{"table": "ai_researcher", "columns": ["funding_round_id", "dst_apid"]}]}, "meta": {"template_id": "sh-spark-sql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"spaceships\").select([\"id\", \"amt\"]).copy_into(\"mental_health_providers\").commit()\n", "labels": {"reads": [{"table": "spaceships", "columns": null}], "writes": [{"table": "mental_health_providers", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ads_products\", writes=\"salmon_farms\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ads_products", "columns": null}], "writes": [{"table": "salmon_farms", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"quick_service.menu_items\").select([\"id\", \"amt\"]).copy_into(\"dws.products_daily\").commit()\n", "labels": {"reads": [{"table": "quick_service.menu_items", "columns": null}], "writes": [{"table": "dws.products_daily", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"dw.campaigns\", limit=5000)\nimport logging\nthreshold = cfg.get('threshold', 0.5)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "dw.campaigns", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nlogger = logging.getLogger(__name__)\nsql = \"INSERT INTO treatment SELECT a.is_male, b.negotiation_date FROM eu_ets a JOIN client b ON a.exit_strategy = b.exit_strategy\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "eu_ets", "columns": null}, {"table": "client", "columns": null}], "writes": [{"table": "treatment", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO incident_regions SELECT day_of_week, line_1, area_size, report_id FROM citydata WHERE day_of_week > 180\"\n", "labels": {"reads": [{"table": "citydata", "columns": ["day_of_week", "line_1", "area_size", "report_id"]}], "writes": [{"table": "incident_regions", "columns": ["day_of_week", "line_1", "area_size", "report_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO newssource SELECT host_id, trend, workoutdate, decoration_theme FROM safetyrecord WHERE host_id > 460\"], check=True)\n", "labels": {"reads": [{"table": "safetyrecord", "columns": ["host_id", "trend", "workoutdate", "decoration_theme"]}], "writes": [{"table": "newssource", "columns": ["host_id", "trend", "workoutdate", "decoration_theme"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO ods.ods_device_log_df SELECT impact_id, gamepreference, complaintid, worker FROM gas_production WHERE impact_id > 256\"\n", "labels": {"reads": [{"table": "gas_production", "columns": ["impact_id", "gamepreference", "complaintid", "worker"]}], "writes": [{"table": "ods.ods_device_log_df", "columns": ["impact_id", "gamepreference", "complaintid", "worker"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO social_impact_scores (student_details, issued_date) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "social_impact_scores", "columns": ["student_details", "issued_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"ads.ads_campaigns_delta\").where(\"dt = current_date()\")\nevents.writeTo(\"ref_transaction_types\").append()\n", "labels": {"reads": [{"table": "ads.ads_campaigns_delta", "columns": null}], "writes": [{"table": "ref_transaction_types", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model esports_participants depends on student_records\ndbt run --select esports_participants --vars '{\"src\":\"student_records\"}'\n", "labels": {"reads": [{"table": "student_records", "columns": null}], "writes": [{"table": "esports_participants", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO projects_pakistan SELECT quality_rank, starting_year FROM community_development WHERE quality_rank > 233\"\n", "labels": {"reads": [{"table": "community_development", "columns": ["quality_rank", "starting_year"]}], "writes": [{"table": "projects_pakistan", "columns": ["quality_rank", "starting_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": "heldout"}}
{"task_type": "SHELL", "content": "# model hospital_beds depends on performance\ndbt run --select hospital_beds --vars '{\"src\":\"performance\"}'\n", "labels": {"reads": [{"table": "performance", "columns": null}], "writes": [{"table": "hospital_beds", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO local_businesses SELECT budget_allocated, num_workers FROM climate_change_impacts_arctic_ocean WHERE budget_allocated > 149\"\n", "labels": {"reads": [{"table": "climate_change_impacts_arctic_ocean", "columns": ["budget_allocated", "num_workers"]}], "writes": [{"table": "local_businesses", "columns": ["budget_allocated", "num_workers"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"debate_people\").where(\"dt = current_date()\")\nevents.writeTo(\"property\").append()\n", "labels": {"reads": [{"table": "debate_people", "columns": null}], "writes": [{"table": "property", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nspark.sql(\"INSERT INTO ods.ods_shipments SELECT movement, score, restaurant, check_in_id FROM food WHERE movement > 176\")\n", "labels": {"reads": [{"table": "food", "columns": ["movement", "score", "restaurant", "check_in_id"]}], "writes": [{"table": "ods.ods_shipments", "columns": ["movement", "score", "restaurant", "check_in_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO instruments SELECT strat_id, condition_id FROM cyber_attacks WHERE strat_id > 102\"\n", "labels": {"reads": [{"table": "cyber_attacks", "columns": ["strat_id", "condition_id"]}], "writes": [{"table": "instruments", "columns": ["strat_id", "condition_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"biotechstartupfunding\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"delivery_routes\")\n", "labels": {"reads": [{"table": "biotechstartupfunding", "columns": null}], "writes": [{"table": "delivery_routes", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\nhive -e \"INSERT INTO user_check_ins SELECT suburb, athlete_id, industry FROM screenings WHERE suburb > 116\"\n", "labels": {"reads": [{"table": "screenings", "columns": ["suburb", "athlete_id", "industry"]}], "writes": [{"table": "user_check_ins", "columns": ["suburb", "athlete_id", "industry"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO tour_types SELECT garment_id, color_description, card_number FROM dws.dws_refunds_full WHERE garment_id > 463\"\n", "labels": {"reads": [{"table": "dws.dws_refunds_full", "columns": ["garment_id", "color_description", "card_number"]}], "writes": [{"table": "tour_types", "columns": ["garment_id", "color_description", "card_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO project_budgets SELECT competition, grant_id, unit_price FROM residential_buildings WHERE competition > 342\"\n", "labels": {"reads": [{"table": "residential_buildings", "columns": ["competition", "grant_id", "unit_price"]}], "writes": [{"table": "project_budgets", "columns": ["competition", "grant_id", "unit_price"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"union_membership_statistics\").select([\"id\", \"amt\"]).copy_into(\"students_in_detention\").commit()\n", "labels": {"reads": [{"table": "union_membership_statistics", "columns": null}], "writes": [{"table": "students_in_detention", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM broadcast\"\n", "labels": {"reads": [{"table": "broadcast", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.manufacturer > 30).all()\n# src table: ai_recs\nengine.execute(\"INSERT INTO healthcare_access SELECT * FROM ai_recs\")\n", "labels": {"reads": [{"table": "ai_recs", "columns": null}], "writes": [{"table": "healthcare_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO customer_event_notes SELECT organisation_type_description, screening_id, genderid FROM eu_ets WHERE organisation_type_description > 322\"\n", "labels": {"reads": [{"table": "eu_ets", "columns": ["organisation_type_description", "screening_id", "genderid"]}], "writes": [{"table": "customer_event_notes", "columns": ["organisation_type_description", "screening_id", "genderid"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nexport TZ=Asia/Shanghai\nRETRIES=${RETRIES:-3}\nsqoop import --connect \"$JDBC\" --table content --target-dir /tmp/land\n", "labels": {"reads": [{"table": "content", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO healthcare_providers SELECT professional_development_programs, programarea FROM moviebudgets WHERE professional_development_programs > 91\"))\n", "labels": {"reads": [{"table": "moviebudgets", "columns": ["professional_development_programs", "programarea"]}], "writes": [{"table": "healthcare_providers", "columns": ["professional_development_programs", "programarea"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"crane\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"techniques\")\n", "labels": {"reads": [{"table": "crane", "columns": null}], "writes": [{"table": "techniques", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO stadium (quantity_sold, calories) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "stadium", "columns": ["quantity_sold", "calories"]}]}, "meta": {"template_id": "py-cursor-execute", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\ntrap 'echo failed' ERR\nhive -e \"INSERT INTO stg_exposure_delta SELECT quantity_sold, fundingdate, productcategory, prof_num FROM worker WHERE quantity_sold > 61\"\n", "labels": {"reads": [{"table": "worker", "columns": ["quantity_sold", "fundingdate", "productcategory", "prof_num"]}], "writes": [{"table": "stg_exposure_delta", "columns": ["quantity_sold", "fundingdate", "productcategory", "prof_num"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "metrics.append(round(score, 4))\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO habitat1 SELECT a.showid, b.taxi_model FROM ods.ods_device_log_df a JOIN medication b ON a.sessionid = b.sessionid\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "ods.ods_device_log_df", "columns": null}, {"table": "medication", "columns": null}], "writes": [{"table": "habitat1", "columns": null}]}, "meta": {"template_id": "py-sql-var-indirect", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"customer_complaints\").where(\"dt = current_date()\")\nevents.writeTo(\"emergency_incidents\").append()\n", "labels": {"reads": [{"table": "customer_complaints", "columns": null}], "writes": [{"table": "emergency_incidents", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mkdir -p /tmp/joblog\nhive -e \"INSERT INTO deliveries SELECT stu_lname, temp FROM oceans WHERE stu_lname > 85\"\n", "labels": {"reads": [{"table": "oceans", "columns": ["stu_lname", "temp"]}], "writes": [{"table": "deliveries", "columns": ["stu_lname", "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": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ods.ods_device_log_df\", limit=5000)\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "ods.ods_device_log_df", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO solar_installations SELECT * FROM legacy\ncur.execute(\"SELECT event_details, co_id FROM church LIMIT 325\")\n", "labels": {"reads": [{"table": "church", "columns": ["event_details", "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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO open_pedagogy_resources SELECT tickets_sold, effort_id, destination_name FROM species WHERE tickets_sold > 181\"\n", "labels": {"reads": [{"table": "species", "columns": ["tickets_sold", "effort_id", "destination_name"]}], "writes": [{"table": "open_pedagogy_resources", "columns": ["tickets_sold", "effort_id", "destination_name"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"launches\")\nsrc.write.insertInto(\"autonomous_vehicles\", overwrite=True)\n", "labels": {"reads": [{"table": "launches", "columns": null}], "writes": [{"table": "autonomous_vehicles", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO biotechstartupfunding SELECT a.fare_date, b.end_station_id FROM donationdates a JOIN african_region_table b ON a.animal_id = b.animal_id\"\n", "labels": {"reads": [{"table": "donationdates", "columns": null}, {"table": "african_region_table", "columns": null}], "writes": [{"table": "biotechstartupfunding", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO broadcast SELECT parameters, meal_date, supplier FROM event_types WHERE parameters > 257\"))\n", "labels": {"reads": [{"table": "event_types", "columns": ["parameters", "meal_date", "supplier"]}], "writes": [{"table": "broadcast", "columns": ["parameters", "meal_date", "supplier"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "RETRIES=${RETRIES:-3}\necho \"job start: $(date +%F)\"\nhive -e \"INSERT INTO transactions SELECT labor_id, all_home, course_description FROM worker_salaries WHERE labor_id > 60\"\n", "labels": {"reads": [{"table": "worker_salaries", "columns": ["labor_id", "all_home", "course_description"]}], "writes": [{"table": "transactions", "columns": ["labor_id", "all_home", "course_description"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"dw_payments_daily\", writes=\"african_region_table\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dw_payments_daily", "columns": null}], "writes": [{"table": "african_region_table", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table artifactcounts --columns plant_name,update_date --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "artifactcounts", "columns": ["plant_name", "update_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"recipe\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "recipe", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO playlist_tracks SELECT birthday, plantlocation FROM dwd_shipments_full WHERE birthday > 54\"\n", "labels": {"reads": [{"table": "dwd_shipments_full", "columns": ["birthday", "plantlocation"]}], "writes": [{"table": "playlist_tracks", "columns": ["birthday", "plantlocation"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model casesattorneys depends on therapeutic_areas\ndbt run --select casesattorneys --vars '{\"src\":\"therapeutic_areas\"}'\n", "labels": {"reads": [{"table": "therapeutic_areas", "columns": null}], "writes": [{"table": "casesattorneys", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"customer_complaints\", writes=\"treatment_type\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "customer_complaints", "columns": null}], "writes": [{"table": "treatment_type", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM buses\", conn)\ndf.to_sql(\"dws.dws_cart_item_di\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "buses", "columns": null}], "writes": [{"table": "dws.dws_cart_item_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": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT emergency_type, development_name FROM water_treatment_plant_upgrades LIMIT 235\")\nthreshold = cfg.get('threshold', 0.5)\nif not rows:\n    logger.warning('empty result')\nmetrics.append(round(score, 4))\nspark.sql(\"INSERT INTO aquaculture_zones SELECT state_province_county, donorgender, fine FROM medication WHERE state_province_county > 352\")\n", "labels": {"reads": [{"table": "water_treatment_plant_upgrades", "columns": ["emergency_type", "development_name"]}, {"table": "medication", "columns": ["state_province_county", "donorgender", "fine"]}], "writes": [{"table": "aquaculture_zones", "columns": ["state_province_county", "donorgender", "fine"]}]}, "meta": {"template_id": "py-multi-statement", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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 investigative_journalism SELECT reported, author_community, report, facid FROM delivery_route_locations WHERE reported > 399\")\n", "labels": {"reads": [{"table": "delivery_route_locations", "columns": ["reported", "author_community", "report", "facid"]}], "writes": [{"table": "investigative_journalism", "columns": ["reported", "author_community", "report", "facid"]}]}, "meta": {"template_id": "py-spark-sql-inline", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT investment_amount, festival_name FROM supplychain\", engine)\nretries = int(os.environ.get('RETRIES', '3'))\ndf.to_sql(\"visitors\", engine, if_exists=\"append\", index=False)\n", "labels": {"reads": [{"table": "supplychain", "columns": ["investment_amount", "festival_name"]}], "writes": [{"table": "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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO category SELECT vendor_name, payment_type_code FROM member_demographics WHERE vendor_name > 173\"\n", "labels": {"reads": [{"table": "member_demographics", "columns": ["vendor_name", "payment_type_code"]}], "writes": [{"table": "category", "columns": ["vendor_name", "payment_type_code"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.production_bopd > 128).all()\n# src table: farmwatertemp\nengine.execute(\"INSERT INTO addresses SELECT * FROM farmwatertemp\")\n", "labels": {"reads": [{"table": "farmwatertemp", "columns": null}], "writes": [{"table": "addresses", "columns": null}]}, "meta": {"template_id": "py-sqlalchemy-orm", "rule_covered": true, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT ride_id, incident_type_description FROM dws.dws_sessions_df LIMIT 246\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\nthreshold = cfg.get('threshold', 0.5)\nimport logging\n", "labels": {"reads": [{"table": "dws.dws_sessions_df", "columns": ["ride_id", "incident_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": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO supplierfabric SELECT providerid, transaction_product FROM budgetallocations WHERE providerid > 389\"], check=True)\n", "labels": {"reads": [{"table": "budgetallocations", "columns": ["providerid", "transaction_product"]}], "writes": [{"table": "supplierfabric", "columns": ["providerid", "transaction_product"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"cultural_sites\").where(\"dt = current_date()\")\nevents.writeTo(\"visitors\").append()\n", "labels": {"reads": [{"table": "cultural_sites", "columns": null}], "writes": [{"table": "visitors", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO age_groups SELECT dance_form, min_temperature_f FROM lanthanumshipments WHERE dance_form > 493\"\n", "labels": {"reads": [{"table": "lanthanumshipments", "columns": ["dance_form", "min_temperature_f"]}], "writes": [{"table": "age_groups", "columns": ["dance_form", "min_temperature_f"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"allergy_type\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\nresult = value * ratio + offset\nretries = int(os.environ.get('RETRIES', '3'))\n", "labels": {"reads": [{"table": "allergy_type", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = get_frame(ctx, \"dept_locations\")\npush_to_store(df, \"project_budgets\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "dept_locations", "columns": null}], "writes": [{"table": "project_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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO cultural_tourists SELECT years_played, menu_id, method_id, pets_allowed_yn FROM union_membership_statistics WHERE years_played > 285\"\n", "labels": {"reads": [{"table": "union_membership_statistics", "columns": ["years_played", "menu_id", "method_id", "pets_allowed_yn"]}], "writes": [{"table": "cultural_tourists", "columns": ["years_played", "menu_id", "method_id", "pets_allowed_yn"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO debate SELECT operation_id, facultyid, annual_carbon_offsets, application_date FROM oregondispensaries WHERE operation_id > 82;\nEOF\n", "labels": {"reads": [{"table": "oregondispensaries", "columns": ["operation_id", "facultyid", "annual_carbon_offsets", "application_date"]}], "writes": [{"table": "debate", "columns": ["operation_id", "facultyid", "annual_carbon_offsets", "application_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"ocean_trenches\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"customer_orders\")\n", "labels": {"reads": [{"table": "ocean_trenches", "columns": null}], "writes": [{"table": "customer_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": "heldout"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nsqoop import --connect \"$JDBC\" --table agroecology --target-dir /tmp/land\n", "labels": {"reads": [{"table": "agroecology", "columns": null}], "writes": []}, "meta": {"template_id": "sh-sqoop-import", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"threat_actors\", limit=5000)\nlogger = logging.getLogger(__name__)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "threat_actors", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = extract_dataset(ctx, \"safety_test_results\")\nwrite_to_sink(df, \"trend_popularity\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "safety_test_results", "columns": null}], "writes": [{"table": "trend_popularity", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"problems\").where(\"dt = current_date()\")\nevents.writeTo(\"donors\").append()\n", "labels": {"reads": [{"table": "problems", "columns": null}], "writes": [{"table": "donors", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model mart_events_delta depends on skills_required_to_fix\ndbt run --select mart_events_delta --vars '{\"src\":\"skills_required_to_fix\"}'\n", "labels": {"reads": [{"table": "skills_required_to_fix", "columns": null}], "writes": [{"table": "mart_events_delta", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"renewable.projects\").toPandas()\ndf[[\"call_id\", \"card_type_code\"]].to_sql(\"things\", engine, index=False)\n", "labels": {"reads": [{"table": "renewable.projects", "columns": null}], "writes": [{"table": "things", "columns": ["call_id", "card_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"ref_transaction_types\").select([\"id\", \"amt\"]).copy_into(\"trend_popularity\").commit()\n", "labels": {"reads": [{"table": "ref_transaction_types", "columns": null}], "writes": [{"table": "trend_popularity", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = pd.read_sql(\"SELECT * FROM biosensor\", conn)\ndf.to_sql(\"sessions\", conn, if_exists=\"replace\", index=False)\n", "labels": {"reads": [{"table": "biosensor", "columns": null}], "writes": [{"table": "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": "heldout"}}
{"task_type": "PYTHON", "content": "logger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nsql = \"INSERT INTO threat_intelligence_data SELECT a.numhearings, b.movie_id FROM investigative_journalism a JOIN trench_depths b ON a.beds = b.beds\"\nspark.sql(sql)\n", "labels": {"reads": [{"table": "investigative_journalism", "columns": null}, {"table": "trench_depths", "columns": null}], "writes": [{"table": "threat_intelligence_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"tech_transactions\").select([\"id\", \"amt\"]).copy_into(\"local_businesses\").commit()\n", "labels": {"reads": [{"table": "tech_transactions", "columns": null}], "writes": [{"table": "local_businesses", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO moviebudgets SELECT text_of_notes, habitat_id, vol_id, airport_id FROM ticket_prices WHERE text_of_notes > 439\"], check=True)\n", "labels": {"reads": [{"table": "ticket_prices", "columns": ["text_of_notes", "habitat_id", "vol_id", "airport_id"]}], "writes": [{"table": "moviebudgets", "columns": ["text_of_notes", "habitat_id", "vol_id", "airport_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": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"mental_health_providers\", writes=\"dw.dw_refunds_delta\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "mental_health_providers", "columns": null}], "writes": [{"table": "dw.dw_refunds_delta", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO techniques SELECT satellite_id, artist_name, artist_nationality FROM addresses WHERE satellite_id > 340\"\n", "labels": {"reads": [{"table": "addresses", "columns": ["satellite_id", "artist_name", "artist_nationality"]}], "writes": [{"table": "techniques", "columns": ["satellite_id", "artist_name", "artist_nationality"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"ods.ods_shipments\")\nsrc.write.insertInto(\"dws.dws_cart_item_di\", overwrite=True)\n", "labels": {"reads": [{"table": "ods.ods_shipments", "columns": null}], "writes": [{"table": "dws.dws_cart_item_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": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO criminal_database (title, vol_id) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "criminal_database", "columns": ["title", "vol_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": "heldout"}}
{"task_type": "SHELL", "content": "# model bi_clicks_daily depends on public_transportation\ndbt build --select bi_clicks_daily --vars '{\"src\":\"public_transportation\"}'\n", "labels": {"reads": [{"table": "public_transportation", "columns": null}], "writes": [{"table": "bi_clicks_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": "heldout"}}
{"task_type": "SHELL", "content": "# model bi_vendors_daily depends on ocean_basin\ndbt run --select bi_vendors_daily --vars '{\"src\":\"ocean_basin\"}'\n", "labels": {"reads": [{"table": "ocean_basin", "columns": null}], "writes": [{"table": "bi_vendors_daily", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"enroll\").where(\"dt = current_date()\")\nevents.writeTo(\"african_region_table\").append()\n", "labels": {"reads": [{"table": "enroll", "columns": null}], "writes": [{"table": "african_region_table", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"vehicle_safety\").where(\"dt = current_date()\")\nevents.writeTo(\"citydata\").append()\n", "labels": {"reads": [{"table": "vehicle_safety", "columns": null}], "writes": [{"table": "citydata", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"baseball_players\", limit=5000)\nresult = value * ratio + offset\nmetrics.append(round(score, 4))\n", "labels": {"reads": [{"table": "baseball_players", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO ref_document_types SELECT energy_generated, community, cultural_competency_score FROM dw.dw_refunds_delta WHERE energy_generated > 447\"))\n", "labels": {"reads": [{"table": "dw.dw_refunds_delta", "columns": ["energy_generated", "community", "cultural_competency_score"]}], "writes": [{"table": "ref_document_types", "columns": ["energy_generated", "community", "cultural_competency_score"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"haircareproducts\", writes=\"member_workout_date\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "haircareproducts", "columns": null}], "writes": [{"table": "member_workout_date", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"autonomous_vehicles\").select([\"id\", \"amt\"]).copy_into(\"reviewer\").commit()\n", "labels": {"reads": [{"table": "autonomous_vehicles", "columns": null}], "writes": [{"table": "reviewer", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "retries = int(os.environ.get('RETRIES', '3'))\nif not rows:\n    logger.warning('empty result')\nspark.sql(\"INSERT INTO safetyrecord SELECT audienceid, organisation_type_description, form_type_code, wage FROM dws.exposure_hourly WHERE audienceid > 100\")\n", "labels": {"reads": [{"table": "dws.exposure_hourly", "columns": ["audienceid", "organisation_type_description", "form_type_code", "wage"]}], "writes": [{"table": "safetyrecord", "columns": ["audienceid", "organisation_type_description", "form_type_code", "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": "heldout"}}
{"task_type": "PYTHON", "content": "spark.sql(\"SELECT initiative_id, opened_date FROM cargoships LIMIT 160\")\nresult = value * ratio + offset\nspark.sql(\"INSERT INTO attorneys SELECT date_complaint_raised, strategy, vaccine_type FROM tech_transactions WHERE date_complaint_raised > 318\")\n", "labels": {"reads": [{"table": "cargoships", "columns": ["initiative_id", "opened_date"]}, {"table": "tech_transactions", "columns": ["date_complaint_raised", "strategy", "vaccine_type"]}], "writes": [{"table": "attorneys", "columns": ["date_complaint_raised", "strategy", "vaccine_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": "heldout"}}
{"task_type": "PYTHON", "content": "import logging\nmetrics.append(round(score, 4))\nthreshold = cfg.get('threshold', 0.5)\nspark.sql(\"INSERT INTO menus SELECT property_price, truck_licence_number, event_date FROM ads.exposure_daily WHERE property_price > 313\")\n", "labels": {"reads": [{"table": "ads.exposure_daily", "columns": ["property_price", "truck_licence_number", "event_date"]}], "writes": [{"table": "menus", "columns": ["property_price", "truck_licence_number", "event_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"performance\").select([\"id\", \"amt\"]).copy_into(\"bi.bi_clicks_daily\").commit()\n", "labels": {"reads": [{"table": "performance", "columns": null}], "writes": [{"table": "bi.bi_clicks_daily", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "msg = \"would run: INSERT INTO problems 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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO buildingtypes SELECT technology, program_date, forest_id, job_title FROM ads.ads_users_delta WHERE technology > 326\"))\n", "labels": {"reads": [{"table": "ads.ads_users_delta", "columns": ["technology", "program_date", "forest_id", "job_title"]}], "writes": [{"table": "buildingtypes", "columns": ["technology", "program_date", "forest_id", "job_title"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO bi.cart_item SELECT cropid, funder, undergraduate, network_name FROM elections WHERE cropid > 219\"\n", "labels": {"reads": [{"table": "elections", "columns": ["cropid", "funder", "undergraduate", "network_name"]}], "writes": [{"table": "bi.cart_item", "columns": ["cropid", "funder", "undergraduate", "network_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"organisation_types\").select([\"id\", \"amt\"]).copy_into(\"stg.stg_exposure_df\").commit()\n", "labels": {"reads": [{"table": "organisation_types", "columns": null}], "writes": [{"table": "stg.stg_exposure_df", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO ads_exposure_daily SELECT image_data, architect_id, athlete FROM urban.buildings WHERE image_data > 485\"))\n", "labels": {"reads": [{"table": "urban.buildings", "columns": ["image_data", "architect_id", "athlete"]}], "writes": [{"table": "ads_exposure_daily", "columns": ["image_data", "architect_id", "athlete"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"europe_org\").where(\"dt = current_date()\")\nevents.writeTo(\"project_outcomes\").append()\n", "labels": {"reads": [{"table": "europe_org", "columns": null}], "writes": [{"table": "project_outcomes", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO crop SELECT phone_number, claim_id, stat_id FROM budgetallocation WHERE phone_number > 214\"\n", "labels": {"reads": [{"table": "budgetallocation", "columns": ["phone_number", "claim_id", "stat_id"]}], "writes": [{"table": "crop", "columns": ["phone_number", "claim_id", "stat_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"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": "heldout"}}
{"task_type": "PYTHON", "content": "df = load_source(ctx, \"donationdates\")\npush_to_warehouse(df, \"research_outcomes\", mode=\"overwrite\")\n", "labels": {"reads": [{"table": "donationdates", "columns": null}], "writes": [{"table": "research_outcomes", "columns": null}]}, "meta": {"template_id": "py-wrapper-verb", "rule_covered": false, "form_family": "wrapper", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table news_articles --columns customer_name,initiativeid --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "news_articles", "columns": ["customer_name", "initiativeid"]}]}, "meta": {"template_id": "sh-sqoop-export", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "beeline -u \"$HS2_URL\" -e \"INSERT INTO crane SELECT fare_date, draft_details, billing_city FROM oil_spills WHERE fare_date > 317\"\n", "labels": {"reads": [{"table": "oil_spills", "columns": ["fare_date", "draft_details", "billing_city"]}], "writes": [{"table": "crane", "columns": ["fare_date", "draft_details", "billing_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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO union_membership_statistics SELECT booked_count, premises_type, carbon_footprint, height FROM skills_required_to_fix WHERE booked_count > 94\"))\n", "labels": {"reads": [{"table": "skills_required_to_fix", "columns": ["booked_count", "premises_type", "carbon_footprint", "height"]}], "writes": [{"table": "union_membership_statistics", "columns": ["booked_count", "premises_type", "carbon_footprint", "height"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO open_pedagogy_resources SELECT artpieceid, graduate, membership_card, rank FROM national_security_agencies WHERE artpieceid > 25\"))\n", "labels": {"reads": [{"table": "national_security_agencies", "columns": ["artpieceid", "graduate", "membership_card", "rank"]}], "writes": [{"table": "open_pedagogy_resources", "columns": ["artpieceid", "graduate", "membership_card", "rank"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"tech_transactions\", writes=\"news_ratings\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "tech_transactions", "columns": null}], "writes": [{"table": "news_ratings", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqlplus -s etl/\"$ORA_PW\"@orcl <<EOF\nINSERT INTO dw_payments_daily SELECT memory_in_g, albumname, agency_id FROM farm_soil_moisture WHERE memory_in_g > 260;\nEOF\n", "labels": {"reads": [{"table": "farm_soil_moisture", "columns": ["memory_in_g", "albumname", "agency_id"]}], "writes": [{"table": "dw_payments_daily", "columns": ["memory_in_g", "albumname", "agency_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"bi.cart_item\").where(\"dt = current_date()\")\nevents.writeTo(\"landfillcapacity\").append()\n", "labels": {"reads": [{"table": "bi.cart_item", "columns": null}], "writes": [{"table": "landfillcapacity", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"educational_programs\").select([\"id\", \"amt\"]).copy_into(\"dental_clinics\").commit()\n", "labels": {"reads": [{"table": "educational_programs", "columns": null}], "writes": [{"table": "dental_clinics", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"gas_processing_plants\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "gas_processing_plants", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"gas_production\")\nsrc.write.insertInto(\"casesattorneys\", overwrite=True)\n", "labels": {"reads": [{"table": "gas_production", "columns": null}], "writes": [{"table": "casesattorneys", "columns": null}]}, "meta": {"template_id": "py-insert-into", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "src = spark.read.table(\"exoplanets\")\nsrc.write.insertInto(\"dw.payments_full\", overwrite=True)\n", "labels": {"reads": [{"table": "exoplanets", "columns": null}], "writes": [{"table": "dw.payments_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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"network_investments\").select([\"id\", \"amt\"]).copy_into(\"dws.dws_sessions_df\").commit()\n", "labels": {"reads": [{"table": "network_investments", "columns": null}], "writes": [{"table": "dws.dws_sessions_df", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"buildingtypes\")\ndf.filter(\"dt >= '2024-01-01'\").write.mode(\"append\").saveAsTable(\"fish_species\")\n", "labels": {"reads": [{"table": "buildingtypes", "columns": null}], "writes": [{"table": "fish_species", "columns": null}]}, "meta": {"template_id": "py-pyspark-saveastable", "rule_covered": true, "form_family": "chain", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"project_budget\", limit=5000)\nretries = int(os.environ.get('RETRIES', '3'))\nmetrics.append(round(score, 4))\nimport logging\n", "labels": {"reads": [{"table": "project_budget", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO taxi_occupancy (donortype, dname) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "taxi_occupancy", "columns": ["donortype", "dname"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO techniques SELECT org_name, potency, promotionid, watch_time FROM energy_efficiency WHERE org_name > 184\"\n", "labels": {"reads": [{"table": "energy_efficiency", "columns": ["org_name", "potency", "promotionid", "watch_time"]}], "writes": [{"table": "techniques", "columns": ["org_name", "potency", "promotionid", "watch_time"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"lanthanumshipments\", writes=\"incident_responses\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "lanthanumshipments", "columns": null}], "writes": [{"table": "incident_responses", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO flight SELECT used_kb, minutes, investmenttype, customer_type_code FROM financial_wellbeing WHERE used_kb > 357\"))\n", "labels": {"reads": [{"table": "financial_wellbeing", "columns": ["used_kb", "minutes", "investmenttype", "customer_type_code"]}], "writes": [{"table": "flight", "columns": ["used_kb", "minutes", "investmenttype", "customer_type_code"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"dept_locations\").where(\"dt = current_date()\")\nevents.writeTo(\"jeans_sales\").append()\n", "labels": {"reads": [{"table": "dept_locations", "columns": null}], "writes": [{"table": "jeans_sales", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"non_profit_employees\", limit=5000)\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "non_profit_employees", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO eu_ets SELECT zone_id, units_sold, vulnerability_name FROM stg_sessions_daily WHERE zone_id > 272\"\n", "labels": {"reads": [{"table": "stg_sessions_daily", "columns": ["zone_id", "units_sold", "vulnerability_name"]}], "writes": [{"table": "eu_ets", "columns": ["zone_id", "units_sold", "vulnerability_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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO moviebudgets SELECT attribute_id, actual_order_id, fault_status, share_in_percent FROM ai_adoption WHERE attribute_id > 181\"))\n", "labels": {"reads": [{"table": "ai_adoption", "columns": ["attribute_id", "actual_order_id", "fault_status", "share_in_percent"]}], "writes": [{"table": "moviebudgets", "columns": ["attribute_id", "actual_order_id", "fault_status", "share_in_percent"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO claim_headers SELECT code, product_id, session_date FROM services WHERE code > 350\"\n", "labels": {"reads": [{"table": "services", "columns": ["code", "product_id", "session_date"]}], "writes": [{"table": "claim_headers", "columns": ["code", "product_id", "session_date"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"dept_locations\", writes=\"artist_genre\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "dept_locations", "columns": null}], "writes": [{"table": "artist_genre", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"sales_data_last_year\", writes=\"ods.ods_shipments\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "sales_data_last_year", "columns": null}], "writes": [{"table": "ods.ods_shipments", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "presto --server presto01:8080 --catalog hive --execute \"INSERT INTO trained_in SELECT warehouse_id, sales_id, consider_rate FROM ods.ods_events_delta WHERE warehouse_id > 197\"\n", "labels": {"reads": [{"table": "ods.ods_events_delta", "columns": ["warehouse_id", "sales_id", "consider_rate"]}], "writes": [{"table": "trained_in", "columns": ["warehouse_id", "sales_id", "consider_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": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO aquaculture_sites SELECT crs_description, characteristic_id FROM ref_company_types WHERE crs_description > 481\"))\n", "labels": {"reads": [{"table": "ref_company_types", "columns": ["crs_description", "characteristic_id"]}], "writes": [{"table": "aquaculture_sites", "columns": ["crs_description", "characteristic_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"fleet_vessels\").toPandas()\ndf[[\"dapp_name\", \"sponsor_name\"]].to_sql(\"network_investments\", engine, index=False)\n", "labels": {"reads": [{"table": "fleet_vessels", "columns": null}], "writes": [{"table": "network_investments", "columns": ["dapp_name", "sponsor_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": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"dws_shipments\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "dws_shipments", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO treatment_type SELECT athlete, segment_id, financially_capable FROM visitor_stats WHERE athlete > 272\"\n", "labels": {"reads": [{"table": "visitor_stats", "columns": ["athlete", "segment_id", "financially_capable"]}], "writes": [{"table": "treatment_type", "columns": ["athlete", "segment_id", "financially_capable"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"dws.dws_exposure_df\").select([\"id\", \"amt\"]).copy_into(\"actor\").commit()\n", "labels": {"reads": [{"table": "dws.dws_exposure_df", "columns": null}], "writes": [{"table": "actor", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO media.reporters SELECT disease, num_of_staff FROM flu_vaccinations WHERE disease > 36\"\n", "labels": {"reads": [{"table": "flu_vaccinations", "columns": ["disease", "num_of_staff"]}], "writes": [{"table": "media.reporters", "columns": ["disease", "num_of_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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO bi.bi_coupon_use_di SELECT other_hotel_details, case_type FROM virtual_tours_usa WHERE other_hotel_details > 253\"\n", "labels": {"reads": [{"table": "virtual_tours_usa", "columns": ["other_hotel_details", "case_type"]}], "writes": [{"table": "bi.bi_coupon_use_di", "columns": ["other_hotel_details", "case_type"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"project_budgets\").select([\"id\", \"amt\"]).copy_into(\"vesseldocking\").commit()\n", "labels": {"reads": [{"table": "project_budgets", "columns": null}], "writes": [{"table": "vesseldocking", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"dry-run: INSERT INTO musicevents 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": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO community_development SELECT song_release_year, accessible, swimmer_id FROM residential_buildings WHERE song_release_year > 173\"\n", "labels": {"reads": [{"table": "residential_buildings", "columns": ["song_release_year", "accessible", "swimmer_id"]}], "writes": [{"table": "community_development", "columns": ["song_release_year", "accessible", "swimmer_id"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"urban_agriculture\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"donationcategories\")\n", "labels": {"reads": [{"table": "urban_agriculture", "columns": null}], "writes": [{"table": "donationcategories", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO documents_mailed SELECT currency, courtname FROM students_disabilities WHERE currency > 216\"))\n", "labels": {"reads": [{"table": "students_disabilities", "columns": ["currency", "courtname"]}], "writes": [{"table": "documents_mailed", "columns": ["currency", "courtname"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"lending_initiatives\").where(\"dt = current_date()\")\nevents.writeTo(\"retail_union\").append()\n", "labels": {"reads": [{"table": "lending_initiatives", "columns": null}], "writes": [{"table": "retail_union", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "echo \"job start: $(date +%F)\"\nhive -e \"INSERT INTO healthcare_providers SELECT health_equity_metric_1, customer_status_code, minister FROM debate_people WHERE health_equity_metric_1 > 405\"\n", "labels": {"reads": [{"table": "debate_people", "columns": ["health_equity_metric_1", "customer_status_code", "minister"]}], "writes": [{"table": "healthcare_providers", "columns": ["health_equity_metric_1", "customer_status_code", "minister"]}]}, "meta": {"template_id": "sh-hive-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"co_ownerships\")\ndf = df.filter(df.status == \"OK\")\ndf.write.mode(\"overwrite\").saveAsTable(\"ocean_basin\")\n", "labels": {"reads": [{"table": "co_ownerships", "columns": null}], "writes": [{"table": "ocean_basin", "columns": null}]}, "meta": {"template_id": "py-read-save-table", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "mysql -h db01 -uetl -p\"$PW\" -e \"INSERT INTO news_ratings (scientific_name, detention_summary) VALUES (%s, %s)\"\n", "labels": {"reads": [], "writes": [{"table": "news_ratings", "columns": ["scientific_name", "detention_summary"]}]}, "meta": {"template_id": "sh-mysql-e", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model newssource depends on therapeutic_areas\ndbt run --select newssource --vars '{\"src\":\"therapeutic_areas\"}'\n", "labels": {"reads": [{"table": "therapeutic_areas", "columns": null}], "writes": [{"table": "newssource", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "sqoop export --connect \"$JDBC\" --table defendants --columns animal_name,starting_year --export-dir /warehouse/stage\n", "labels": {"reads": [], "writes": [{"table": "defendants", "columns": ["animal_name", "starting_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": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ref_transaction_types\", writes=\"plant_safety_protocols\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ref_transaction_types", "columns": null}], "writes": [{"table": "plant_safety_protocols", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT coupon_amount, menuid FROM mart.mart_events_delta LIMIT 108\")\nrows = cur.fetchall()\nlogger = logging.getLogger(__name__)\nretries = int(os.environ.get('RETRIES', '3'))\nthreshold = cfg.get('threshold', 0.5)\n", "labels": {"reads": [{"table": "mart.mart_events_delta", "columns": ["coupon_amount", "menuid"]}], "writes": []}, "meta": {"template_id": "py-cursor-select", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model attorneys depends on vehicle_safety\ndbt run -s attorneys --vars '{\"source_table\":\"vehicle_safety\"}'\n", "labels": {"reads": [{"table": "vehicle_safety", "columns": null}], "writes": [{"table": "attorneys", "columns": null}]}, "meta": {"template_id": "sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "# TODO: 旧逻辑 INSERT INTO ancient_ceramics SELECT * FROM legacy\ncur.execute(\"SELECT last_inspection_date, total_horses FROM well LIMIT 424\")\n", "labels": {"reads": [{"table": "well", "columns": ["last_inspection_date", "total_horses"]}], "writes": []}, "meta": {"template_id": "py-commented-sql", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO dissolved_oxygen_readings SELECT permit_id, launch_year, transaction_value, site_name FROM recyclingprograms WHERE permit_id > 367\"))\n", "labels": {"reads": [{"table": "recyclingprograms", "columns": ["permit_id", "launch_year", "transaction_value", "site_name"]}], "writes": [{"table": "dissolved_oxygen_readings", "columns": ["permit_id", "launch_year", "transaction_value", "site_name"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "conn = psycopg2.connect(dsn)\ncur = conn.cursor()\ncur.execute(\"INSERT INTO mlb_teams_mascots (movie, property_price) VALUES (%s, %s)\", (uid, amt))\nconn.commit()\n", "labels": {"reads": [], "writes": [{"table": "mlb_teams_mascots", "columns": ["movie", "property_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": "heldout"}}
{"task_type": "PYTHON", "content": "import subprocess\nsubprocess.run([\"hive\", \"-e\", \"INSERT INTO stg.stg_refunds SELECT transaction_category, cultural_significance, allergy FROM ads.ads_cart_item_di WHERE transaction_category > 377\"], check=True)\n", "labels": {"reads": [{"table": "ads.ads_cart_item_di", "columns": ["transaction_category", "cultural_significance", "allergy"]}], "writes": [{"table": "stg.stg_refunds", "columns": ["transaction_category", "cultural_significance", "allergy"]}]}, "meta": {"template_id": "py-subprocess-hive", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO user_check_ins SELECT driverid, donortype, jobtitle, spacecraft FROM digitalexperiences WHERE driverid > 213\"\n", "labels": {"reads": [{"table": "digitalexperiences", "columns": ["driverid", "donortype", "jobtitle", "spacecraft"]}], "writes": [{"table": "user_check_ins", "columns": ["driverid", "donortype", "jobtitle", "spacecraft"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO salesrevenue SELECT a.room_count, b.team_id_br FROM ai_adoption a JOIN resourcemanagement b ON a.affiliation = b.affiliation\"\n", "labels": {"reads": [{"table": "ai_adoption", "columns": null}, {"table": "resourcemanagement", "columns": null}], "writes": [{"table": "salesrevenue", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "spark-sql --master yarn -e \"INSERT INTO heritage_tours SELECT singer_id, plant, volume_id, min_dew_point_f FROM content WHERE singer_id > 108\"\n", "labels": {"reads": [{"table": "content", "columns": ["singer_id", "plant", "volume_id", "min_dew_point_f"]}], "writes": [{"table": "heritage_tours", "columns": ["singer_id", "plant", "volume_id", "min_dew_point_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": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO brand_scores SELECT a.number_thousands, b.class_president_vote FROM education_aid a JOIN individuals b ON a.fan_id = b.fan_id\"\n", "labels": {"reads": [{"table": "education_aid", "columns": null}, {"table": "individuals", "columns": null}], "writes": [{"table": "brand_scores", "columns": null}]}, "meta": {"template_id": "sh-psql-c", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"users\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "users", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model cart_item_daily depends on documents_mailed\ndbt run --select cart_item_daily --vars '{\"src\":\"documents_mailed\"}'\n", "labels": {"reads": [{"table": "documents_mailed", "columns": null}], "writes": [{"table": "cart_item_daily", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "psql -h \"$PGHOST\" -U etl -c \"INSERT INTO bi.bi_coupon_use_di SELECT a.porphyria, b.vr_platform FROM intelligencebudget a JOIN policies b ON a.centerid = b.centerid\"\n", "labels": {"reads": [{"table": "intelligencebudget", "columns": null}, {"table": "policies", "columns": null}], "writes": [{"table": "bi.bi_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": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"freight\").where(\"dt = current_date()\")\nevents.writeTo(\"weatherdata\").append()\n", "labels": {"reads": [{"table": "freight", "columns": null}], "writes": [{"table": "weatherdata", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "frame = warehouse_client.fetch(table=\"ref_transaction_types\", limit=5000)\nimport logging\nresult = value * ratio + offset\n", "labels": {"reads": [{"table": "ref_transaction_types", "columns": null}], "writes": []}, "meta": {"template_id": "h-py-kwarg-table", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"dwd.device_log_daily\").where(\"dt = current_date()\")\nevents.writeTo(\"autonomous_taxis\").append()\n", "labels": {"reads": [{"table": "dwd.device_log_daily", "columns": null}], "writes": [{"table": "autonomous_taxis", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "df = spark.read.table(\"attractions\")\ntbl = f\"dw.tmp_{ds_nodash}\"\ndf.write.saveAsTable(tbl)\n", "labels": {"reads": [{"table": "attractions", "columns": null}], "writes": []}, "meta": {"template_id": "py-dynamic-fstring", "rule_covered": true, "form_family": "py", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO gold_mines SELECT card_id, accessibility FROM investigative_journalism WHERE card_id > 177\"\n", "labels": {"reads": [{"table": "investigative_journalism", "columns": ["card_id", "accessibility"]}], "writes": [{"table": "gold_mines", "columns": ["card_id", "accessibility"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "rows = session.query(Src).filter(Src.booking_id > 249).all()\n# src table: emergency_incidents\nengine.execute(\"INSERT INTO community_development.schools SELECT * FROM emergency_incidents\")\n", "labels": {"reads": [{"table": "emergency_incidents", "columns": null}], "writes": [{"table": "community_development.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": "heldout"}}
{"task_type": "PYTHON", "content": "Repo.of(\"ferry_routes\").select([\"id\", \"amt\"]).copy_into(\"bi.bi_orders\").commit()\n", "labels": {"reads": [{"table": "ferry_routes", "columns": null}], "writes": [{"table": "bi.bi_orders", "columns": null}]}, "meta": {"template_id": "h-py-orm-chain-dsl", "rule_covered": false, "form_family": "orm", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "# model public_buildings depends on animaleducation\ndbt run --select public_buildings --vars '{\"src\":\"animaleducation\"}'\n", "labels": {"reads": [{"table": "animaleducation", "columns": null}], "writes": [{"table": "public_buildings", "columns": null}]}, "meta": {"template_id": "h-sh-dbt-run", "rule_covered": false, "form_family": "config", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "from sqlalchemy import text\nwith engine.begin() as conn:\n    conn.execute(text(\"INSERT INTO salaries SELECT crs_code, users_engaged, serve_id FROM bi_exposure_daily WHERE crs_code > 194\"))\n", "labels": {"reads": [{"table": "bi_exposure_daily", "columns": ["crs_code", "users_engaged", "serve_id"]}], "writes": [{"table": "salaries", "columns": ["crs_code", "users_engaged", "serve_id"]}]}, "meta": {"template_id": "h-py-sqlalchemy-text", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"ods.ods_shipments\", writes=\"explainability_report\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "ods.ods_shipments", "columns": null}], "writes": [{"table": "explainability_report", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "cur.execute(\"SELECT state_code, replacement_cost FROM dws.dws_vendors_daily LIMIT 424\")\nrows = cur.fetchall()\nif not rows:\n    logger.warning('empty result')\n", "labels": {"reads": [{"table": "dws.dws_vendors_daily", "columns": ["state_code", "replacement_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": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO mental_health_facilities SELECT address, feedtype, diplomacy_id, watch_time FROM hospital_beds WHERE address > 38\"\n", "labels": {"reads": [{"table": "hospital_beds", "columns": ["address", "feedtype", "diplomacy_id", "watch_time"]}], "writes": [{"table": "mental_health_facilities", "columns": ["address", "feedtype", "diplomacy_id", "watch_time"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "TBL=\"ads_report_${BIZ_DATE}\"\nhive -e \"INSERT INTO $TBL SELECT * FROM bi_exposure_daily\"\n", "labels": {"reads": [{"table": "bi_exposure_daily", "columns": null}], "writes": []}, "meta": {"template_id": "sh-dynamic-var", "rule_covered": true, "form_family": "sh", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "clickhouse-client --host ch01 --query \"INSERT INTO casesattorneys SELECT creation, manufacturer_id, opid FROM mature_forest WHERE creation > 402\"\n", "labels": {"reads": [{"table": "mature_forest", "columns": ["creation", "manufacturer_id", "opid"]}], "writes": [{"table": "casesattorneys", "columns": ["creation", "manufacturer_id", "opid"]}]}, "meta": {"template_id": "h-sh-clickhouse", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "@pipeline(reads=\"satellite_missions\", writes=\"unique_donors\")\ndef run(src):\n    return transform(src)\n", "labels": {"reads": [{"table": "satellite_missions", "columns": null}], "writes": [{"table": "unique_donors", "columns": null}]}, "meta": {"template_id": "h-py-decorator-pipeline", "rule_covered": false, "form_family": "decorator", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "PYTHON", "content": "events = spark.table(\"london_buses\").where(\"dt = current_date()\")\nevents.writeTo(\"recyclingamount\").append()\n", "labels": {"reads": [{"table": "london_buses", "columns": null}], "writes": [{"table": "recyclingamount", "columns": null}]}, "meta": {"template_id": "h-py-spark-table-alias", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}
{"task_type": "SHELL", "content": "impala-shell -i impalad01 -q \"INSERT INTO cargo SELECT component_type, violation_count, built_year FROM bi_exposure_daily WHERE component_type > 355\"\n", "labels": {"reads": [{"table": "bi_exposure_daily", "columns": ["component_type", "violation_count", "built_year"]}], "writes": [{"table": "cargo", "columns": ["component_type", "violation_count", "built_year"]}]}, "meta": {"template_id": "h-sh-impala", "rule_covered": false, "form_family": "h", "source_dataset": "synth+gretelai/synthetic_text_to_sql+b-mc2/sql-create-context", "split_group": "heldout"}}